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COMMUNICATION 480 Culture, Self-Organization and Swarm Intelligence
Spring 2009 Professor Dr. Gary Fontaine Office 326 Crawford Hall Phone 956-3335; Email <[email protected]> Home Page "www2.hawaii.edu/~fontaine/garyspag.html" Office hours 9:00-10:15 MTW or by arrangement Course syllabus & resource links at “www2.hawaii.edu/~fontaine/ucourses.html” Course Graphics at "www2.hawaii.edu/~fontaine/480swarmPP.ppt" Laulima https://laulima.hawaii.edu/portal; COM-480-001 [MAN.87120.SP09] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This seminar examines current theories, research, applications and interventions associated with emerging "evolutionary" (as opposed to "rational") models of optimization strategies presented by work in biology, evolutionary programming, genetic algorithms, engineering, the social/behavioral sciences and business involving the related fields of self-organization in biological systems and swarm intelligence. Particular emphasis is on the implications of this work for culture and the role of communication in dealing with diversity in the completion of collaborative tasks. As a seminar, the expectation is that all participants will contribute actively to our learning relevant to this topic in both structured and unstructured ways. The material itself is diverse and challenging. We are going to "swarm" our topics to optimize our understanding of them and our ability to apply that understanding to our society and ourselves. Assignments (1) Each student/team will lead class presentation & discussion of an assigned reading/resource in terms of key themes, concepts and questions (e.g., what don't we know). While all teams are encouraged to present material from additional resources related to their topic, each team with a graduate student member will be required to do so. Typically the entire class session will be available for each presentation/discussion. (2) It is expected that every student will have completed each assigned reading/resource by the day of the presentation/discussion of at least those topics from Kennedy & Eberhart (2001). Students will be responsible for providing their perspective on each along with that of the assigned discussion leaders. The sum of those contributions will represent their class contribution grade. (3) Each student will submit an analysis of a real or simulated organizational setting or culture in terms of the role-played by self-organization and/or
swarming and the implications of such for optimization. Include in the analysis description of the setting/culture, its functions, the methods of observation or simulation, data collection and analysis, and a discussion of the implications of the findings for topics of relevance to this course. Grading: Student team led presentation/discussion 30% Setting/Culture Analysis class presentation 20% (April 20 - May 4)
Setting/Culture Analysis written assignment 30% (due by May 11, 11:45 am)
In-class contributions 20% Final grade: 90-100%=A; 80-89%=B; 70-79%=C; 60-69%=D; 0-59%=F Required reading Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco:
Morgan Kaufmann. Highly recommended reading Axelrod, R. (1997). The dissemination of culture: A model with local convergence and global
polarization, Journal of Conflict Resolution, 41, 203-226. Reprinted in R. Axelrod (1997), The Complexity of Cooperation. Princeton, NJ: Princeton University Press.
Blum, C. & Merkle, D. (2008). Swarm Intelligence: Introduction and Applications. Berlin: Springer. Bonabeau, E. & Meyer, C. (2001). Swarm Intelligence: A Whole New Way to Think About
Business. Harvard Business Review, May, 107-114. Camazine, S, Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G. and Bonabeau, E.
(2001). Self-Organization in Biological Systems. Princeton, NJ: Princeton University Press.
Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, MA: MIT Press.
Crichton, M. (2002). Prey. New York: Avon Books. Epstein, J. M. & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom
Up. Cambridge, MA: The MIT Press. Lakomski, G. (2001). Organizational change, leadership and learning: culture as cognitive
process. The International Journal of Educational Management, 15(2), 68-77. Mendes, R., Kennedy, J. & Neves, J. (2004). The fully informed particle swarm: Simpler, maybe
better. IEEE Transactions of Evolutionary Computation, 8(3). [Distributed in class] Michalewicz, Z., Schmidt, M., Michalewiczt, M. & Constantin, C. (2007). Adaptive Business
Intelligence. Berlin: Springer. Smith, E. R. (in press). Social relationships and groups: New insights on embodied and
distributed cognition. (Prepared for a special issue of Cognitive Systems Research on "Perspectives on Social cognition" edited by L. Marsh).
COM480 SCHEDULE Spring 2009; m/w 10:30 - 11:45; BUSAD E202
Week of-- January 12
• Overview--firefly stories; 691 (A Sense of Presence in Face-to-Face, Online and Virtual Environments), smart dust & ambient intelligence; Prey & Self-Organization in Biological systems, shells; firefly explanation; course goal; syllabus; note statistics appendix & relevance to “communication.”
• Self-org demo & Bai Wang Park; assign Student teams & readings; January 19
• Holiday • SB
January 26
• Self-org & Swarm key concepts--Beijing (2005), Jinan (2006), Singapore (2007), SOC (2007) "The game".
• Self-org & Swarm key concepts (continued) February 2
• Fontaine--K&E Preface & #1 "Models and concepts of life and intelligence" • Fontaine--K&E #2 "Symbols, connections, & optimization by trial & error"
February 9
• Fontaine--K&E #3 "On our nonexistence as entities: the social organism" • Fontaine--K&E #4 "Evolutionary Computation Theory and Paradigms"
February 16
• Holiday • Student Team 1--K&E #5 "Humans--actual, imagined, and implied"
February 23
• Student Team 2--K&E #6 "Thinking is social" • Student Team 3--K&E #7 "The particle swarm"
March 2
• Student Team 4--K&E #8 "Variations and comparisons" • Student Team 5--K&E #10 Bonabeau & Meyer
March 9
• Student Team 6--Axelrod • Student Team 7--Epstein &Axtell
March 16
• Student Team 8--Lakomski • Student Team 9--Smith
March 23
• Holiday Spring Break • Holiday Spring Break
March 30
• Texas A&M • Texas A&M
April 6
• Global Swarming -- Jinan/Singapore/Dubai • Diversity -- Beijing & Knowledge Paradox -- Penang
April 13
• Effective Leadership & Decision-Making; Fully informed particle swarm • Open
April 20
• Student presentations of analyses of settings/culture (15 min) (5) • Student presentations of analyses of settings/culture (5)
April 27
• Student presentations of analyses of settings/culture (5) • Student presentations of analyses of settings/culture (5)
May 4
• Student presentations of analyses of settings/culture (3) • Wrap-up
Final Exam May 11, 9:45-11:45
Some Self-Organization & Swarm Intelligence References & Online Sources
Axelrod, R. (1997). The dissemination of culture: A model with local convergence
and global polarization, Journal of Conflict Resolution, 41, 203-226. Reprinted in R. Axelrod (1997), The Complexity of Cooperation. Princeton, NJ: Princeton University Press.
Axelrod, R. (2003). Advancing the Art of Simulation in the Social Sciences.
Japanese Journal for Management Information Systems, Special Issue on Agent-Based Modeling, 12(3).
Beer, M., Eisenstat, R. & Spector, B. (1990). Why change programs don’t
produce change. Harvard Business Review, 68, 158-166. Blum, C. & Merkle, D. (2008). Swarm Intelligence: Introduction and Applications.
Berlin: Springer. Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From
Natural to Artificial Systems. New York: Oxford University Press. Bonabeau, E. & Meyer, C. (2001). Swarm Intelligence: A Whole New Way to
Think About Business. Harvard Business Review, May, 107-114. Boyd, J. P. & Johnson, J. C. (1995). Finding N-culture consensus with simulated
annealing. Mathematical Social Sciences, 30, 97 (Abstract). Boyd, R. & Richarson, P. J. (1985). Culture and the Evolutionary Process.
Chicago: University of Chicago Press. Brabazon, T. & Matthew, R. (2002). Organizational Adaptation on Rugged
Landscapes. Kingston University's Centre for international business policy. Camazine, S, Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G. and
Bonabeau, E. (2001). Self-Organization in Biological Systems. Princeton, NJ: Princeton University Press, 143-145.
Caporael, L. (1997). The evolution of truly social cognition: The core
configurations model. Personality and Social Psychology Review, 1, 276-298.
Cho, Sung-Bae (2006). Intelligent System Design for episodic Memory on
Mobile Daily Life. The Sixth International conference on Intelligent system Design and Applications. Jinan, China.
Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, MA: MIT Press.
Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. (2005). Effective leadership
and decision-making in animal groups on the move. Nature 433(Feb. 3): 513-516. Abstract available at http://dx.doi.org/10.1038/nature03236.
Crichton, M. (2002). Prey. New York: Avon Books. Epstein, J. M. & Axtell, R. (1996). Growing Artificial Societies: Social Science
from the Bottom Up. Cambridge, MA: The MIT Press. Farrell, B. & Twining-Ward, L. (2005). Seven steps towards sustainability:
Tourism in the context of new knowledge. Journal of Sustainable Tourism, 13(2), 109-122.
Festinger, L. (1954) A theory of social comparison processes, Human Relations,
7, 117-40. Fogel, L. J. (1995). Evolutionary Computation: Toward a New Philosophy of
Machine Intelligence. Piscataway, NK: IEEE Press. Fontaine, G. (2003). The “Knowledge Paradox” in Global Management: Local
versus Global Assignment Strategies. International Journal of Knowledge, Culture and Change Management, Vol. 3, 659-669. http://garyfontaine.cgpublisher.com/product/pub.28/prod.26
Fontaine, G. (2005). A Self-Organization Perspective on the Impact of Local
verses Global Assignment Strategies and Knowledge Building. International Journal of Diversity in Organisations, Communities and Nations, 5(1), 57-66. http://garyfontaine.cgpublisher.com/product/pub.29/prod.183
Fontaine, G. (2006). Global Swarming. Proceedings of the Sixth International
Conference on Intelligent System Design and Applications (ISDA'06), 1212-1215. www2.computer.org/portal/web/csdl/doi/10.1109/ISDA.2006.253785
Fröhlich, F (1996) Neural Networks with Java. Fachhochschule Regensburg,
Department of Computer Science http://rfhs8012.fh-regensburg.de/%7Esaj39122/jfroehl/diplom/e-index.html
Goethals, G. R., & Darley, J. M. (l977). Social comparison theory: An attributional
approach. In J. M. Suls, & R. L. Miller, Social comparisons processes: Theoretical and empirical perspectives. Washington, DC:
Graham, C. A. and W. C. McGrew 1980. Menstrual synchrony in female
Undergraduates living on a coeducational campus.
Psychoneuroendocrinology 5: 245-252. Gurney, K. (1997). An Introduction to Neural Networks. London:UCL Press.
Gurney, K. (1997). An Introduction to Neural Networks. London:UCL Press. Henrich, J. & Boyd, R. (1998). The evolution of conformist transmission and the
emergence of between-group difference. Evolution and Human Behavior, 19, 215-242.
Holland, J. (1998). Emergence: From Chaos to Order. Reading, MA: Perseus
Books. Kauffman, S. A. (1995). At Home in the Universe: The Search for the Laws of
Self-Organization and Complexity. New York: Oxford University Press. Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco:
Morgan Kaufmann. Kennedy, J. (2006). The Particle Swarm: Individual and Collective Intelligence.
The Sixth International conference on Intelligent system Design and Applications. Jinan, China.
Klarreich, Erica (2006). The Mind of the Swarm. Math explains how group
behavior is more than the sum of its parts. Science News (Nov. 25) 170:22, 347-349.
Lakomski, G. (2001). Organizational change, leadership and learning: culture as
cognitive process. The International Journal of Educational Management, 15(2), 68-77.
Langton, C. G. (1991). Computation at the edge of chaos: Phase transitions and
emergent computation. In S. Forrest (Ed.), Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks. Cambridge, MA: The MIT Press.
Latane, B. & L'Herrou, T. (1996). Spatial clustering in the conformity game:
Dynamic social impact in electronic groups. Journal of Personality and Social Psychology, 70, 1218-1230.
Latane, B., Liu, J. H., Nowak, A., Bonevento, M. & Zheng, L. (1995). Distance
matters: Physical space and social impact. Personality and Social Psychology Bulletin, 21, 795-805.
Mendes, R. & Neves, J. (2004). What makes a successful society? Experiments
with population topologies in particle swarms. Advances in Artificial Intelligence: XVII Brazilian symposium on Artificial Intelligence--SBIA'04.
Mendes, R., Kennedy, J. & Neves, J. (2004). The fully informed particle swarm:
Simpler, maybe better. IEEE Transactions of Evolutionary Computation, 8(3).
Michalewicz, Z., Schmidt, M., Michalewiczt, M. & Constantin, C. (2007). Adaptive
Business Intelligence. Berlin: Springer. Millonas, M. M. (1993). Swarms, phase transitions, and collective intelligence.
In C. Langton (Ed.), Artificial Life III, 417-445. Reading, MA: Addison-Wesley.
Ohsawa, Yukio (2006). Chance Discovery: Data-Based Decision for systems
Design. The Sixth International conference on Intelligent system Design and Applications. Jinan, China.
Parrott, D. & Li X. (2004). A particle swarm model for tracking multiple peaks in a
dynamic environment using speciation. Proceeding of the 2004 Congress on Evolutionary Computation, USA, pp. 98-103.
Peng, K. & Nisbett, R. E. (1999). Culture, dialectics, and reasoning about
contradiction. American Psychologist, 54, 741-755. Reggia, J. A., Schulz, R., Wilkinson, G. S. & Uriagereka, J. (2001). Conditions
enabling the evolution of inter-agent signaling in an artificial world. MIT: Artificial Life 7, 3-32.
Riva, G., Loreti, P., Lunghi, M. Vatalaro, F. & Davide, F. (2002). Presence 2010:
The emergence of ambient intelligence. In Riva, Davide & Ijsselsteijn , Being there: concepts, effects and measurements of user presence in synthetic environments. Amsterdam, the Netherlands: IOS Press. www.emergingcommunication.com/volume5.html
Rucker, R. (1999). Seek! New York: Four Walls Eight Windows. Smith, E. R. (2008). Social relationships and groups: New insights on embodied
and distributed cognition. Cognitive Systems Research, 9(1-2), 24-32 Smith, E. R. & Semin, G. R. (2004). Socially situated cognition: Cognition in its
social context. Advances in Experimental Social Psychology, 36, 53-117. Smith, L. (2001). An Introduction to Neural Networks. Centre for Cognitive and
Computational Neuroscience, Department of Computing and Mathematics, University of Sterling, Scotland, UK.
Suls, L, Martin, R. & Wheeler, L. (2002). Social Comparison: Why, With Whom, and With What Effect? Current Directions in Psychological Science, 11(5), 159.
Suls, L. & Wheeler, L. (2000). Handbook of Social Comparison: Theory &
Research. NY: Kluwer Academic/Plenum. Thibault, J. W. & Kelley, H. H. (1952). The Social Psychology of Groups. New
York: John Wiley & Sons. Wolfram, S. (1994). Cellular Automata and Complexity: Collected Papers.
Reading, MA: Addison-Wesley. Online Sources Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence Swarm Intelligence www.engr.iupui.edu/~eberhart/web/PSObook.html The Game by Icosystem at //icosystem.com/game.htm Introduction to StarLogo //education.mit.edu/starlogo/ Swarm intelligence resources http://dsp.jpl.nasa.gov/members/payman/swarm/ Welcome to Particle Swarm Central www.particleswarm.info/ Evolutionary Systems and Artificial Life informatics.indiana.edu/rocha/alife.html SwarmWiki www.swarm.org/wiki/Main_Page Alife.org "International Society of Artificial life" www.alife.org/ "Zooland" The artificial life resource alife.ccp14.ac.uk/zooland/zooland/ Neural networks with Java by Jochen Fröhlich http://rfhs8012.fh-regensburg.de/%7Esaj39122/jfroehl/diplom/e-index.html SmartDust: Autonomous sensing and communication in a cubic millimeter robotics.eecs.berkeley.edu/~pister/SmartDust/ White, T. (1997). Swarm Intelligence: A Gentle Introduction with applications. www.sce.carleton.ca/netmanage/tony/swarm-presentation/index.htm. [ppt slide show].
Student Teams & Topics & Tentative Dates Student Team 1 (February 18)--K&E #5 "Humans--actual, imagined, and implied" Purington, O'Neil, Matsumoto Student Team 2 (February 23)--K&E #6 "Thinking is social" Otsuka, Oshiro, Shepard Student Team 3 (February 25)--K&E #7 "The particle swarm" Shimabukuro, Stams, Medieros Student Team 4 (March 2)--K&E #8 "Variations and comparisons" Estes, Sumitomo, Student Team 5 (March 4)--Bonabeau & Meyer “Swarm Intelligence: A Whole New Way to Think About Business.” [National Geographic "Swarm theory"] Takayesu, Yoshida, Santos Student Team 6 (March 9)--Axelrod “The dissemination of culture: A model with local convergence and global polarization. [Shibanai, Yasuno & Ishiguro "Effects of Global Information Feedback on Diversity: Extensions to Axelrod's Adaptive Culture Model"] Akiona, Seko Student Team 7 (March 11)--Epstein & Axtell “Growing Artificial Societies: Social Science from the Bottom Up.” Simmons, San Jose Student Team 8 (March 16)--Lakomski “Organizational change, leadership and learning: culture as cognitive process.” Taira, Student Team 9 (April 18)--Smith “Social relationships and groups: New insights on embodied and distributed cognition.” [Smith & Semin "Socially situated cognition: Cognition in its social context" Crockett, Reeves ~~~~~~~~~~~~~~~~~~~~~~~ Axelrod, R. (1997). The dissemination of culture: A model with local convergence and global
polarization, Journal of Conflict Resolution, 41, 203-226. Reprinted in R. Axelrod (1997), The Complexity of Cooperation. Princeton, NJ: Princeton University Press. [Distributed in class]
Bonabeau, E. & Meyer, C. (2001). Swarm Intelligence: A Whole New Way to Think About
Business. Harvard Business Review, May, 107-114. [Distributed in class] Epstein, J. M. & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom
Up. Cambridge, MA: The MIT Press. [Distributed in class] Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann. Lakomski, G. (2001). Organizational change, leadership and learning: culture as cognitive
process. The International Journal of Educational Management, 15(2), 68-77. [Distributed in class]
Smith, E. R. (2008). Social relationships and groups: New insights on embodied and distributed
cognition. Cognitive Systems Research, 9(1-2), 24-32. [Distributed in class]
Order for 10-minute presentation of Setting/Culture Analysis
Communication Seminar COM 480 001 (CRN: 87120) Class Meets: MW, 1030-1145, in BUSAD E202, 01/12 thru 05/15/2009 Enrollment: 23 of 20 (-3 seats available) ID StudentName Institution Major Email Reg GradeMode SubmittedGrade FinalGrade 15345087 Akiona, Kristi L. MAN Communication [email protected] RW Standard Letter A-F 15577024 Crockett, Charmaine C. MAN Communication [email protected] RE Standard Letter A-F 10697002 Dunaway, David C. MAN Unclassified [email protected] RW Standard Letter A-F 16405851 Estes, John K. MAN Communication [email protected] RE Standard Letter A-F 15574534 Matsumoto, Grant M. MAN Communication [email protected] RW Standard Letter A-F 15697277 Medeiros, Casey K. MAN Religion [email protected] RW Standard Letter A-F 15717324 O'Neill, Jason M. MAN Communication [email protected] RW Standard Letter A-F 16241415 Oshiro, Kristen M. MAN Communication [email protected] RE Standard Letter A-F 16589259 Otsuka, Yusuke MAN Communication [email protected] RW Standard Letter A-F 15626946 Purington, Jasmine N. MAN Communication [email protected] RE Standard Letter A-F 17491887 Reeves, Sarah J. MAN Communication [email protected] RW Standard Letter A-F 16517656 San Jose, David L. MAN Communication [email protected] RW Standard Letter A-F 17495460 Santos, Jacob A. MAN Communication [email protected] RE Standard Letter A-F 14507116 Seko, Kimberly E. MAN Communication [email protected] RW Standard Letter A-F 17148994 Shepard, Cindy A. MAN Communication [email protected] RW Standard Letter A-F 15462433 Shimabukuro, Erin M. MAN Communication [email protected] RW Standard Letter A-F 11171242 Simmons, Judith B. MAN Communication [email protected] RW Standard Letter A-F 16898322 Stams, Elizabeth C. MAN Communication [email protected] RW Standard Letter A-F 10602740 Sumitomo, Russell T. MAN Communication [email protected] RW Standard Letter A-F
16630507 Taira, Kazufumi MAN Communication [email protected] RW Standard Letter A-F 13533324 Takayesu, Jenny E. MAN Communication [email protected] RW Standard Letter A-F 10957348 Tamakawa, Jan M. MAN Communication [email protected] RW Standard Letter A-F 11370823 Yoshida, Evan M. MAN Communication [email protected] RW Standard Letter A-F 16630507 Taira, Kazufumi MAN Communication [email protected] RW Standard Letter A-F 13533324 Takayesu, Jenny E. MAN Communication [email protected] RW Standard Letter A-F 10957348 Tamakawa, Jan M. MAN Communication [email protected] RW Standard Letter A-F 11370823 Yoshida, Evan M. MAN Communication [email protected]
Grades for leading seminar discussions COM691 Fall 2007
February 14 • K&E #3 "On our nonexistence as entities: the social organism" K&E #4 team
1 19/20 February 21 • K&E #5 "Humans--actual, imagined, and implied" team 2 19/20
February 28 • K&E #6 "Thinking is social" team 3 18/20
March 7 • K&E #7 "The particle swarm" team 4 19/20
March 14 • K&E #8 "Variations and comparisons" team 5 19/20
March 28 • K&E #10 "Implications and speculations" team 6 19/20
April 4 • E&A #s 1, 2, 3 & 4 team 7 20/20
April 11 • Bonabeau & Meyer team 8 18/20
April 18 • Lakomski team 9 19/20
April 25 • Axelrod team 10 20/20
Overview Readings firefly stories 691 (A Sense of Presence in Face-to-Face, Online and Virtual Environments) smart dust & ambient intelligence Prey & Self-Organization in Biological systems, shells; firefly explanation self-org demo Bai Wang Park course goal syllabus assign readings/discussion leaders note statistics appendix.
Notes Crichton, M. (2002). Prey. New York: Avon Books.
Mai said, "And the swarm decided this on its own?" "Yes," I said. "Although I'm not sure 'decided' is the right term. The emergent behavior is the sum of individual agent behaviors. There isn't anybody there to 'decide' anything. There's not brain, no higher control in that swarm." "Group mind?" Mae said. "Hive mind?" "In a way," I said. "The point is this, there is no central control." "But it looks so controlled," she said. "It looks like a defined, purposeful organism." "Yeah, well, so do we," Charley said, with a harsh laugh. Nobody else laughed with him. (p. 360)
***************** If you want to think of it that way, a human being is actually a giant swarm. Or more precisely, it's a swarm of swarms, because each organ--blood, liver, kidneys--is a separate swarm. What we refer to as a 'body' is really the combination of all these organ swarms. We think our bodies are solid, but that's only because we can't see what is going on at the cellular level. If you could enlarge the human body, blow it up to a vast size, you would see that it was literally nothing but a swirling mass of cells and atoms, clustered together into smaller swirls of cell and atoms. Who cares? Well, it turns out a lot of processing occurs at the level of the organs. Human behavior is determined in many places. The control of our behavior is not located in our brains. It's all over our bodies. So you could argue that 'swarm intelligence' rules human beings, too. Balance is controlled by the cerebellar swarm and rarely comes to consciousness. Other processing occurs in the spinal cord, the stomach, the intestine A lot of vision takes place in the eyeball, long before the brain is involved. ... So there's an argument that the whole structure of consciousness, and the humans sense of self-control and purposefulness, is a user illusion. We don't have conscious control over ourselves at all. We just think we do. Just because human beings went around thinking of themselves as 'I' didn't mean that it was true. And for all we knew, this damned swarm had some
sort of rudimentary sense of itself as an entity. If, if it didn't, it might very soon start to." (pp. 361-362)
Fireflies Over 2000 species of fireflies occur worldwide. One species, in particular, distributed throughout South and Southeast Asia has evolved an extraordinary luminescent behavior. It is illustrated in this early account from an observer--
"Imagine a tree thirty-five to forty feet high thickly covered with small ovate leaves, apparently with a firefly on every leaf and all the fireflies flashing in perfect unison at the rate of about three times in two seconds, the tree being in complete darkness between the flashes. Imagine a dozen such trees standing close together along the river's edge with synchronously flashing fireflies on every leaf. Imagine a tenth of a mile of river front with an unbroken line of ... trees with fireflies on every leaf flashing in synchronism, the insects on the trees at the ends of the line acting in perfect unison with those in between. Then, if one's imagination is sufficiently vivid, he may form some conception of this amazing spectacle" (Smith, 1935).
I first encountered it within a few days of my arrival in my wife's village in the mountains of Panay in the central Philippines. Looking through the open window at night one evening I was awed by the spectacle of hundreds of thousands or perhaps millions of flashing fireflies. While I had, of course, encountered fireflies elsewhere in the world I had never seen anything quite like this--clouds of fireflies flashing randomly among certain trees distributed around a rice paddy, maybe a few thousand square meters in area. The spectacle was intriguing and my first thought was that fireflies must certainly be the origin of fairies and this clearly was a "banquet" of fairies. I was feeling somewhat proud of all this insight, and then. And then. And then rather of a sudden it seemed all the fireflies around all of the trees began flashing all at once. In apparently perfect unison. I was astounded! Then--and now--because there was no electricity in this mountain community it was otherwise total black at night. So when the fireflies flashed it was like an immense strobe light going off every second and the light flooded the river valley in waves. I had never experienced anything remotely like it. I said to myself (excuse the profanity) "Who the fuck is leading this!" "Who is in control?" I had--otherwise--absolutely no explanation. And as I've now found, until very recently, neither has science. It has in fact been a major scientific mystery. It a very meaningful way it has--through a rather circuitous route lead to my presenting this paper here today. When explorers and naturalists began coming back with these stories of synchronously flashing fireflies, they were absolutely disbelieved. Fireflies could do no such massively, self-organized, coordinated thing (Camazine, et al., 2001). Only people, with leaders or a plan could, right?
Our course goal From the perspective of self-organization and swarm intelligence evolution is basically a search engine--it is a locally-based, trial and error process for identifying optimal solutions to problems presented by an ecology. From this perspective, not only is evolution such a process, but so is mind. In the former it involves determining which biological features fit best a landscape. In the latter it is which patterns of beliefs, attitudes and behaviors are optimal for survival and effectiveness in an ecology. That is, which "cultures" work best for which ecologies. I trust that by the end of this course we will all understand the perspective presented by the material we examine-- Every person, every team, every culture can be viewed as a potential solution to a challenge presented by the ecology in which we live and work--some are more optimal for some ecologies than others, but all are potential solutions to some challenge in some ecology. And in times of ecological change, particularly rapid ecological change--at the edge of chaos--a diversity of solutions is critical to optimization--to evolution, to success, to survival.
Examples of Synchronized Rhythmic Activity Organism Process Coupling Mode Reference Hydrozoans coordinated mechanical/ Makie 1973 movement electrical Sponges sperm release unknown Reiswig 1970 Honey bees synchronized unknown Moritz & Southwick 1992
respiration
Abalones sperm/egg chemical Morse 1993 release Fiddler crab claw waving visual Blackwell et al. 1998 Herring gulls breeding unknown Darling 1938 Human females synchronized pheromonal Russell et al. 1980 menstrual cycles
(adapted from Camazine et al., 2001, pp. 163-164).
Demonstration of Synchronization in Humans Demonstration I: The person conducting the demonstration instructs participants to close their eyes and hold a coin in their fingers. Participants wait for the instructor to tap his or her coin on the tabletop. Participants then tap their coins as quickly as possible. This is a test of reaction time performed as a group. Very few participants can respond in less than 150 ms. Demonstration 2: The instructor gives the participants these simple instructions: "Close your eyes and start tapping your coin on the tabletop in a comfortable, regular rhythm trying to synchronize with your neighbors." With these instructions an audience of several hundred people will synchronize within a few cycles, usually at a frequency of 2 to 3 taps/s. More remarkably, the lime interval between the first taps and the last taps in a volley are generally less than 130 ms, shorter than the reaction time just demonstrated! What is the mechanism for synchrony in demonstration 2? As the reaction time data confirm, the participants cannot be waiting to hear a neighbor's tap before initiating their own tap. The explanation offered by Buck and Buck (1976) is that each person must have "measured the passage of time up to the instant when it was necessary to initiate the neural message that caused his fingers to move in synchrony with the lingers of all the other participants." Thus, based on the subject's perception of the rhythm of the taps, the subject anticipates the time to the next tap and factors in the motor delay between the brain and finger. This is the "anticipatory time-measuring" or "sense of rhythm" explanation earlier suggested by Buck and Buck (1968) for the mechanism of rhythmic synchronous flashing of fireflies. We should now be better able to appreciate the difference between the mechanism of synchrony used by humans in finger tapping and the mechanism of firefly flash synchronization. The cognitive processes that humans use to synchronize finger tapping are not those used by fireflies. Among fireflies, the perception of a neighbor's photic signal automatically resets an oscillator in the firefly's brain and triggers a flash. Among humans, there is no oscillator for an arbitrary tapping rhythm. Humans do indeed have a sense of rhythm, they can measure time intervals and anticipate when to initiate a motor message to tap. (Adapted from (Camazine et al., 2001).
The Game by Icosystem at http://icosystem.com/game.htm
The Game provides a practical demonstration of the following points:
• Simple rules of individual behavior can lead to surprisingly coherent system level results.
• Small changes in rules or in the way they are applied can have significant impact on the system level results.
• Intuition can be a particularly poor guide to prediction of the behavior of complex systems above a few levels of complexity (here we have only 3).
• Simulation is a powerful tool for understanding the dynamics of complex systems.
You can play The Game for real with 10 or more participants--
Ask everyone to each randomly select 2 individuals - person A and person B. Now ask the participants to move so that they always keep A in between themselves and B - so that A is their protector from B. Everyone in the room will mill about in a seemingly random fashion and will soon begin to ask why they are doing this. Now tell them to stop, and that they are now the protector so tell them to move so that they keep themselves in between A and B. The results are striking. Almost instantaneously the whole room will implode on itself with everyone clustering together in a tight knot.
By using a simple agent-based simulation in which each person is modeled as an autonomous agent following the rules, one can actually predict the emergent collective behavior (see The Game). Also, by using the simulation as test bed, one can explore the design of the rules to produce a desired outcome. (Challenge: Can you drive the simulation to form 2 clusters?)
The Game requires a Java-enabled browser.
How do the termites do it "Some of the largest and most sophisticated of all animal structures are the mounds built by African termites, the fungus growers. These castles of clay, relative to the individual termites that helped build them, are air-conditioned skyscrapers immensely larger and arguably more sophisticated than the vast majority of human buildings" (Camazine et al., 2001, p. 158). "These termites are able to build elaborate domed structures that are begun as pillars; in the course of building, the pillars are tilted toward one another until their tops touch and they form an arch. Connecting arches results in the typical dome. As it is frequently remarked that the invention of the arch was a major milestone in the development of the architecture of civilized man, we might wonder how in the world a swarm of simple-minded termites could accomplish the feat. If we were building an arch, we would start with a plan, that is, a central representation of the goal and the steps leading to it. Then, as the work would probably require more than one person …, a team of workers would be organized, with the architect or someone who understands the plan supervising the laborers, telling them where to put materials, controlling the timing of the ascension of the two pillars and their meeting. We are so dependent on centralized control of complex functions that it is sometimes impossible for us to understand how the same task could be accomplished by a distributed, noncentralized system. Termites build a dome by taking some dirt in their mouths, moistening it, and following these rules:
1. Move in the direction of the strongest pheromone concentration.
2. Deposit what you are carrying where the smell is strongest.
After some random movements searching for a relatively strong pheromone field, the termites will have started a number of small pillars. The pillars signify places where a greater number of termites have recently passed, and thus the pheromone concentration is high there. The pheromone dissipates with time, so in order for it to accumulate, the number of termites must exceed some threshold; they must leave pheromones faster than the chemicals evaporate. This prevents the formation of a great number of pillars, or of a wasteland strewn with little mouthfuls of dirt. The ascension of the pillars results from a … positive feedback cycle. The greater the number of termites depositing their mouthfuls in a place, the more attractive it is to other termites. … As termite pillars ascend and the termites become increasingly involved in depositing their loads, the pheromone
concentration near the pillars increases. A termite approaching the area then detects the pheromone, and as there are multiple pillars and the termite is steering toward the highest concentration, it is likely to end up in the area between two pillars. It is attracted toward both, and eventually chooses one or the other. As it is approaching the pillar from the region in between, it is more likely to climb up the side of the pillar that faces the other one. As a consequence, deposits tend to be on the inner face of the pillars, and as each builds up with more substance on the facing side, the higher it goes the more it leans toward the other. The result is an arch" (Kennedy & Eberhart, 2001, p. 103-104). There is no central control, the individual termites are unaware of the "plan" they are carrying out, and a rather spectacular mound emerges from their self-organized behavior.
From: [email protected] (Diane Kelly) Subject: Re: women living together and concurrent cycles Date: 8 Oct 93 17:48:19 GMT >In article <[email protected]> [email protected] (Daniel B Case) writes about menstrual synchrony: |> >There was long a lot of anecdotal evidence attesting to this, and |> finally some researchers at the U of Penn checked it out by attempting to |> induce it artificially. They had one control woman wear "sweat pads" in her |> armpits (the assumption was that some sort of pheromone had something to do |> with it) then got the sweat out and mixed it with alcohol, and daubed it |> under the noses of the participating women (none of whom knew or |> associated with the control woman) at regular intervals. In time all |> the studied women came to match the control woman's cycle. There's |> still some controversy about this, but there's at least been some |> research done on it. I imagine at least some of y'all have been wondering why I haven't already thrown my two cents in -- after all, I'm a biologist, I study reproductive physiology, and I'm a woman. Well, it took me a little time to search through the library for some real references... OK, here goes. The first scientific evidence confirming human menstrual synchrony appears in a 1971 paper by Martha McClintock. She interviewed 137 members of her (all women's) dorm several times during a school year, and ended up with the onset date for every woman's period between late September and April. She compared data for roomates, close friends, and the living group as a whole, and found that in each case the mean difference in onset dates decreased as the year progressed. In short, women who spent a lot of time together had their periods start closer and closer together as the school year went on. The experiment also turned out to be repeatable (Graham and McGrew, 1980; Quadagno _et al._, 1981). McClintock suggested that menstrual synchrony might be caused by some sort of pheromone, but also pointed out that it could be any number of other things -- after all, these women lived together, ate the same food (it's a dormitory, remember?), and came in contact with many of the same random influences... in short, the environment was really too uncontrollable to determine an exact cause. But just because McClintock was cautious when suggesting possible causes for menstrual synchrony didn't mean that others wouldn't go looking for that holy grail of a pheromone. That study Daniel mentions above (Russell, _et al._, 1980) grabbed onto the idea that the axillary sweat glands (the ones in your armpits) may be putting out scents that are involved in unconscious human communication, and tried to apply it to menstruation. Now, although many people seem to think that the very idea of us human types putting out scent-messages to each other is ridiculous, there is a fair amount of indirect evidence to back it up. Humans have a specialized type of sweat gland (apocrine sweat glands) located primarily in the axillae (AKA armpits), the anogential region, on the areole of the breast, and in the face and scalp. They secrete steroids related to the sex hormones: notably androsterone and androsterol. Bacteria on our skin break these compounds down into odiferous substances. The secretions from these glands are similar to compounds that are believed to influence behavior in non-human mammals. And the glands are only active after puberty, and become inactive in post-menopausal women (Doty, 1981). It's no surprise that when faced with evidence supporting the existence of human menstrual synchrony, scientists went looking for armpit pheromones. Russell, et al. (1980) did get results suggesting that something secreted in sweat might have an effect on menstruation onset. But some of his methods have been criticized: (1) the sweat donor was one of the scientists doing the experiment, and had contact with the test subjects -- possibly adding an additional influence to her sweat-soaked pads. (2) The sweat donor was a "known inducer" -- she had a history of making other women's cycles conform to her own. Although the
experiment had a 'no sweat' control, it did not have a 'sweat from a non-inducing woman' control, which may have skewed the results. In short, then -- menstrual synchrony does happen in humans, there's some evidence that it may be pheromonally influenced, but no one has isolated the compound. Now, since menstrual synchrony is just the outward sign of _ovulatory_ synchrony, the really interesting question is _why_ a bunch of women would start to ovulate at around the same time... Diane Kelly Duke Zoology Literature cited: Doty, R. L. 1981. Olfactory communication in humans. Chem. Senses 6:351-75. Graham, C. A. and W. C. McGrew 1980. Menstrual synchrony in female undergraduates living on a coeducational campus. Psychoneuroendocrinology 5: 245-252. McClintock, M. K. 1971. Menstrual synchrony and suppression. Nature 229:244-245. Quadagno, D. M., H. E. Shubeita, J. Deck, and D. Francoeur 1981. Influence of male social contacts, exercise, and all-female living conditions on the menstrual cycle. Psychoneuroendocrinology 6: 239-244. Russell, M. J., G. M. Switz, and K. Thompson 1980. Olfactory influences on the human menstrual cycle. Pharmacol. Biochem. Behav. 13: 737-739.
Bonabeau, E. & Meyer, C. (2001). Swarm Intelligence: A Whole New Way to Think about Business. Harvard Business Review, May, 107-114. "The collective behavior that emerges from a group of social insects has been dubbed "swarm intelligence." 108 The advantages of swarm intelligence--"Flexibility: the group can quickly adapt to a changing environment; Robustness: even when one or more individuals fail, the group can still perform its tasks. Self-organization: the group needs relatively little supervision or top-down control." 111 "Through self-organization, the behavior of the group emerges from the collective interactions of all the individuals. In fact, a major recurring theme in swarm intelligence is that even if individuals follow simple rules, the resulting group behavior can be surprisingly complex--and remarkable effective. And, to a large extent, flexibility and robustness result from self-organization." 108
Preface & Chapter 1 – Models and Concepts of Life and Intelligence This chapter first looks at what kinds of phenomena can be included under these terms. What is life? This is an important question of our historical era, as there are many ambiguous cases. Can life be created by man? What is the role of adaptation in life and thought? And why do so many natural adaptive systems seem to rely on randomness? Is cultural evolution Darwinian? – Some think so; the question of evolution in culture is central to this volume. The Game of Life and cellular automata in general are computational examples of emergence, which seems to be fundamental to life and intelligence, and some artificial life paradigms are introduced. The chapter begins to inquire about the nature of intelligence and reviews some of the ways that researchers have tried to model human thought. We conclude that intelligence just means "the qualities of a good mind," which of course might not be defined the same by everybody. Preface K&E Basic assertions PP Basic assertions--
"1. Mind is social. We reject the cognitivistic perspective of mind as an internal, private thing or process and argue instead that both function and phenomenon derive from the interactions of individuals in a social world. (a) Human intelligence results from social interaction. Evaluating, comparing, and imitating one another, learning from experience and emulating the successful behaviors of others, people are able to adapt to complex environments through the discovery of relatively optimal patterns of attitudes, beliefs, and behaviors. Our species' predilection for a certain kind of social interaction has resulted in the development of the inherent intelligence of humans. (b) Culture and cognition are inseparable consequences of human sociality. Culture emerges as individuals become more similar through mutual social learning. The sweep of culture moves individuals toward more adaptive patterns of thought and behaviors. The emergent and immergent phenomena occur simultaneously and inseparably. 2. Central to the concept of computational intelligence (evolutionary computation, fuzzy logic, neural networks, and artificial life) is system adaptation that facilitates intelligent behavior in complex and changing environments. (a) Swarm
intelligence provides a useful computational intelligence paradigm for implementing adaptive systems. (b) Particle swarm optimization is an extension of, and potentially important new incarnation of, cellular automata." xxi
Particle swarm optimization PP "Particle swarm optimization utilizes a "population" of candidate solutions to evolve an optimal or near-optimal solution to a problem. …When the population is initialized, in addition to the variables being given random values, they are stochastically [probabilistically] assigned velocities. Each iteration, each particle's velocity is stochastically accelerated toward its previous best position (where it had its highest fitness value) and toward a neighborhood best position (the position of highest fitness by any particle in its neighborhood)." xvii Swarm--a population of interacting elements that can optimize a global objective through collaborative search of a solution or fitness space. xxvii "In its common usage, "swarm" refers to a disorganized cluster of moving things, usually insects, moving irregularly, chaotically, somehow staying together even while all of them move in apparently random directions. This is a good visual image of what we talk about. ... An insect swarm is a three-dimensional version of something that can take place in a space of many dimensions--a space of ideas, beliefs, attitudes, behaviors, and other things that minds are concerned with." xvi Mind--"That which thinks." A phenomenological aspect (conscious experience of thinking) and psychological aspect the function of thinking). xxvi differentiate from Brain. Artificial neural network--an analysis paradigm or algorithm roughly modeled on the massively parallel structure of the brain and that simulates a highly interconnected, parallel computational structure with many individual processing elements. xxvii The mechanics of life and thought Stochastic adaptation: is anything ever really random 9 Adaptation--fit to the environment. Often used to differentiate the animate from the inanimate. It is a dynamic process of co-adaptation or co-evolution.
Stochastic process--random, as opposed to deterministic; issues of predictability; and importance of randomness for introducing creativity, innovation and chance encounters. Algorithm--a step-by-step procedure for solving a problem The "two great stochastic systems“ PP The "two great stochastic systems" (Gregory Batson) 12 Evolution--operates through variation and selection; a variety of problem solutions (chromosomes or patterns of features) are proposed and tested; those that do well tend to survive. Mind--hypotheses or ideas ("meme's," attitudes, values, theories, cultures, etc.) are proposed (propagated through a population via communication), tested and accepted or rejected, those accepted tend to survive "The evolution of ideas involves changes in the states of minds that hold ideas, not changes in the ideas themselves; it is a search—by minds—through the universe of ideas, to find the fitter ones.“ Evolution “uses selection, removing less fit members from the population. Mind adapts by changing the states of individuals who persist over time. These are two different kinds of adaptation.“ "The day-to-day rules of living together are not especially complicated--some relatively straightforward scraps of wisdom will get you through life well enough. But the accumulated [emergence] effect of these rules is a cultural system of imponderable depth and breadth." The game of life: emergence in complex systems 16 Cellular automata and the edge of chaos 20 Cellular automata--simple virtual machines in 1, 2, 3 or more dimensions consisting of cells in some state (binary or continuous) at step 1. The state of a cell at the next step is determined by its state at step one and the states of cells in it's neighborhood. That determination is based on a set of simple rules. One dimensional Cellular Automata PP One Dimensional Cellular Automata Patterns PP Conus textile pattern PP Simulating self-organization in nature PP (repeat) Two Dimensional Cellular Automata PP
Evolution across CA steps results in 4 states-- Type 1-Homogeneous state (a point attractor) Type 2-Stable or periodic structures (a periodic attractor). Type 3-Chaotic pattern (a strange attractor). Type 4-Complex localized structures, sometimes long-lived, that are dynamic and resemble matter between states, during transition, at the edge of chaos. An attractor is some state or states to which trajectories--or evolution--converges over time Game of life PP Some simple “Game of Life” & Cellular Automata emergent patterns PP Cellular Automata and life PP In Type 4 CA, "certain sets of rules result in patterns that run on and on, maybe forever, creating patterns that are characteristic and identifiable but that never repeat. These patterns look like something produced by nature, like the tangle of branches in a winter wood, the king's crowns of raindrops on a puddle, the wiggling ripples of wind blowing over a pond's surface, the fuzz on a thistle. Looking at the computer screen as a sequence of CA iterations unfolds, you feel a sense of recognizing nature at work." 23 In the same sense that the state of each cell in a "a neuron, depends on the states of the cells it is connected to … the rules of a CA describe patterns of causality, such as exist among parts of many kinds of complex systems. The state of any element in the system — ecology, brain, climate, economy, galaxy — depends causally on the states of other elements, and not just on the states of individual elements, but on the pattern or combination of states. Life and mind are woven on just this kind of causal patterning." 23 The behavior of the system at the edge of chaos is not predictable, and it's not random: it is complex. 20 Emergence--complex structures, or patterns emerge at the global or system level unpredictably from numerous interactions at the local level--an economy, an ecology--and without central control. These patterns are often resilient (if perturbed they typically return to balance). They seem "life-like"
Cellular Automata and Swarms PP Patterns in the CA can occur over time and appear "particle-like" as if a quality were transmitted across the field through changes at successive locations of a medium, though the emergent entity is composed only of changes of the states of those locations. These "particles" sometimes continue through time until they collide with other particles, with the result being dependent on the nature of the particles, the angle of their impact, the state of the neighborhood in which they collided, and other factors. These "particles" are can resemble of the "agents" in the "particle swarm"--any loosely structured collection of somewhat autonomous agents that interact with one another. 25 "An ant colony is a kind of swarm where the agents are ants, highway traffic can be conceptualized as a swarm whose agents are cars and drivers, and so on; all these kinds of swarms can be simulated using this CA-derived software." "An economy might be made up of a swarm of agents who are people, while each person might be made up of a swarm that is their beliefs or even the neurons that provide the infrastructure for their beliefs--swarms within swarms." 25-26
If you want to think of it that way, a human being is actually a giant swarm. Or more precisely, it's a swarm of swarms, because each organ--blood, liver, kidneys--is a separate swarm. What we refer to as a 'body' is really the combination of all these organ swarms.
Artificial life in computer programs 26 Intelligence: good minds in people and machines 30 The Turing test--if the subject can't tell if the computer's responses were generated by the human or the machine, then the computer is considered intelligent. 32 Intelligence--ability of a system to adapt its behavior to meet its goals in a range of environments. 33 Sociogenetic learning--the basic unit of mutability (inheritance) is the idea, with culture being a reservoir of learned behavior and beliefs. Adaptive ideas are retained by culture while poor ideas are forgotten. In ant this only requires time for the pheromone trail to evaporate; in humans the ideas must be forgotten, or replaced, or repressed. Finally, note discussion of implications for science and the scientific method--stressing the importance of "induction," not just deduction, for the
source of ideas or hypotheses--"A scientist resembles an artist more than a calculator." 34
Chapter 2 – Symbols, Connections, and Optimization by Trial and Error This chapter is intended to provide a background that will make the later chapters meaningful. What is optimization and what does it have to do with minds? We describe aspects of complex fitness landscapes, and some methods that are used to find optimal regions on them. Minds can be thought of as points in high-dimensional space: what would be needed to optimize them? Symbols as discrete packages of meaning are contrasted to the connectionist approach where meaning is distributed across a network. Some issues are discussed having to do with numeric representations of cognitive variables and mathematical problems. Symbols in trees and networks 36 Symbol processing and “fuzzy” approaches to intelligence PP
AI (artificial intelligence)--a field in which people try to program machines to act "intelligently." Most commonly use a symbol processing (logical) model of intelligence. Symbol processing paradigm--a problem is embedded in a universe of symbols are seen as discrete units of knowledge and can be manipulated according to universal rules of logic (e.g., syllogistic logic--all A are C; if B is A; then B is C). Discrete decisions are made--to choose or not, something is true or it isn't. "There is necessarily an innate assumption, in the symbol-processing paradigm, that all the relevant facts are known and that their interrelationships are known." A graphical depiction of the symbol processing approach looks like a tree with ever more numerous branches as you progress through the logic. Symbol Trees and Networks PP Fuzzy logic--the truth of propositions (e.g., "facts") vary from zero to one and most are somewhere in between. Truth is "fuzzy." (e.g., some A are sort of C). Tentative decisions are made based on a network of probabilities. "Rules are implemented in parallel, all at once, and the conclusion is obtained by combining the outputs of all the decisions." Get volunteer to do--Peng, K. & Nisbett, R. E. (1999). Culture, dialectics, and reasoning about contradiction. American Psychologist, 54, 741-755. A
comparative study of differences between a Western approach and a more dialectical Chinese approach to logic in solving problems. A graphical depiction of fuzzy logic is more like a network. It allows for the kind of reciprocal causal relations and feedback loops so common in nature. Back-propagation & Recurrent Networks PP Back-propagation & recurrent networks and are those where nodes can affect one another. Positive feedback (or autocatalytic effects) and negative feedback. A network where information passes through from one or more inputs to one or more outputs is a feedforward network. Constraint satisfaction models and neural networks PP In constraint satisfaction models each node is connected to some other nodes by positive or negative links--like correlation coefficients. The state of a node is constrained by (depends on) the states of other nodes and the strengths of the connections. The effect of an active node through a positive link is positive and through a negative link is negative. In other words, the effect is the product of the node state times the constraint value. The goal is to find a pattern of node states that best satisfies the constraints. This harmony function can be calculated over the entire network or separately for each node and summed. It is a form of optimization--looking for input values that minimize or maximize some function, typically minimizing error at the output (∑x2 ). This optimization process can be extremely complicated with conflicting restraints, redundancy, nonlinearity, and noise and some methods are better and more realistic than others. K&K suggest that social forms of optimization are more realistic for cognitive simulations that "inside-the-head" methods (e.g., thought experiments). 41/46 Symmetrical connection weights between binary nodes indicate the likelihood of one being active when the other is (correlation). Asymmetrical connections indicate the likelihood of activation of the node at the head of the arrow as a function of the state of the note at the origin (causality). 34 Harmony maximization is a good way to represent consistency between cognitions in a cognitive dissonance (Festinger) or other cognitive consistency-based model
Neural networks--These networks are called neural networks because they roughly resemble the way cells in the brain are hooked up, and because they are often able to categorize things and make decisions something like a brain does. The living neuron PP
Structure of a neural cell in the human brain
The human brain consists of neural cells that process information. Each cell works like a simple processor and only the massive interaction between all cells and their parallel processing makes the brain's abilities possible. A neuron consists of a core, dendrites for incoming information and an axon with dendrites for outgoing information that is passed to connected neurons. Information is transported between neurons in form of electrical stimulations along the dendrites. Incoming information that reaches the dendrites is added up and then delivered along the axon to the dendrites, where the information is passed to other neurons if it has exceeded a certain threshold. In this case, the neuron is activated. If the incoming stimulation is too low, the information will not be transported any further and the neuron is inhibited. The connections between the neurons are adaptive--the connection structure is changing dynamically. Learning ability of the human brain is based on this adaptation. Learning Learning--"finding values for the connection weights that makes for the accurate estimation of outputs from inputs. … Optimization means that we are looking for input values that minimize or maximize the result of some function ... . Normally in training a … network we want to minimize error at the output, i.e., we want the sums of products to make the best estimate of what y should be." 46
Neural networks work by a kind of statistical analysis of data, they can be more robust in the face of noise and measurement error. They don't assume that symbols or the rules that connect them are hard-and-fast discrete entities, with information flowing through one logical branch or another. They appear to model well human processes such as perception, categorization, learning, memory, and attention, including the errors that humans make." 49 Artificial neural network PP
In a neural net, the neurons are grouped in neuron layers. Usually each neuron of one layer is connected to all neurons of the preceding and the following layer (except the input layer and the output layer of the net). The information given to a neural net is propagated layer-by-layer from input layer to output layer through either none, one or more hidden layers. It is also possible that information is propagated backwards through the net. This is not the general structure of a neural net--some neural nets have no hidden layers or the neurons in a layer are arranged as a matrix. What's common
to all neural net types is the presence of at least one weight matrix, the connections between two neuron layers. Neural networks have been called "universal function approximators" because they can reproduce the outputs of any arbitrarily complex mathematical function. Problem solving and optimization 48 Neural networks with Java PP Optimization PP Both evolution and mind are concerned with "finding patterns that satisfy a complex set of constraints," i.e., fitness with or accommodation to the challenges set by the ecology--an ecological niche or personal, social or cultural constraints. Optimization is a process of adjusting a system get the best possible outcome--goodness of fit or minimizing error. To do that they both rely heavily, if not exclusively, on some version of trial and error. Usually we can't look everywhere for the optimum--we need to limit our search in some practical way. Where parameters — independent variables (IVs)--are truly “independent” of one another the optimization process is just taking multiple instances of a one variable problem (e.g., ANOVA). The fitness landscape looks like a volcano or Mt. Fujiyama sloping gradually upward from all directions toward a single global optimum. When the IVs are actually correlated a good position on one dimension may deteriorate the optimality of values on other dimensions. In this case the fitness landscape looks like a real landscape (e.g. Multiple Regression). 54-55 "While we are talking about the rather academic topic of optimizing mathematical functions, what is being said also applies to the dynamics of evolving species and thinking minds." 52 Fitness Landscapes PP Stuart Kauffman (1996:149ff; c.f. McMaster 1997:157ff) has developed the notion of the fitness landscape, originally suggested by Sewell Wright in the 1930s (Lewin 1993:57). It offers a model of what might be happening in this complex dynamic. Kauffman suggests that a snapshot of an environment at a given time t1 could be thought of as a landscape. If all points in the environment are equally fit, the landscape will be flat like Norfolk or Illinois. If there are differences in fitness between systems, with some very fit and others very unfit, the landscape will resemble the Himalayas. If one system is very much fitter than all the others we
get what is sometimes known as a Fujiyama landscape—one enormous peak in an otherwise relatively flat landscape. High-dimensional cognitive space and word meanings 55 How language is learned How we learn language PP Osgood, Such & Tannenbaum (1957) work with the "Semantic differential" explored meaning space by having people rate concepts on evaluation, potency & activity. They found there is often a "halo effect" or correlation between these "independent" factors. Landauer & Dumais (1997) multidimensional study of semantic space--people learn meanings from hearing words in contexts with other words in a "bottom-up, " rather than "top-down" manner. We don't learn the meaning of words by memorizing dictionaries. We learn from other people. "The symbols themselves are arbitrary, and so different cultures can use different sounds and glyphs to mean the same thing. Yet within a culture meanings are agreed upon. … Thus the psychological issue is one of adaptation and optimization, of finding our way through labyrinthine landscapes of meaning and mapping related regions appropriately." 60 Two factors of complexity: NK Landscapes 60 Combinatorial optimization 64 Complexity of a landscape, evolution and mind PP Complexity of a landscape or system--making optimization hard—determined by N, the dimensionality of the system, and K, the amount of interconnectedness of the elements that make it up." (Kauffman, 1991, 1995) As N increases, the number of possible states of the system increases exponentially--combinatorial explosion--each new binary element that is added doubles the patterns of node activations. It quickly becomes impossible to test all possible combinations--we need a good algorithm for the optimal allocation trials. Heuristics are shortcuts that reduce the size of the space that needs to be examined."
“Evolution can be seen as a kind of search of the space of possible life-forms, where various adaptations are tried, continuing into the future. An important aspect of this search is that it is conducted in parallel; that is, a number of mutations and genetic combinations are proposed and tested simultaneously throughout the populations of species.“ "Minds as well search their space of ideas in parallel … Various cultures explore their own regions of a cognitive parameter space, and within those cultures particular individuals search particular regions of the vast search space of beliefs, attitudes, and behaviors. Here and there a culture hits a dead end, and cultures interact, passing heuristical information back and forth to one another, and out on the little branches individuals convey information to one another that shapes their searches. … Information sharing is our main heuristic for searching the large space of possible explanations for the world." Binary optimization 67 A bitstring can represent a base-two number, for instance, the number 10011 represents--going from right to left and increasing the multipliers by powers of 2--(1x1) + (1x2) + (0x4) + (0x8) + (1x16) = 19. Because 0 and 1 are discrete states, they can be used to encode qualitative, nonnumeric variables as well as numeric ones.68 Searching for optimal solutions (again) PP Two basic strategies for searching for optimal solutions in a "solution landscape" or “fitness landscape“(an array of potential solutions) to problems in an ecology— Exploration involves a broad sampling of alternative solutions across a variety of regions looking for the overall global optimum. A common exploration strategy is a "random walk" throughout the solution "landscape" hoping to hit upon the highest point, the best strategy, an Everest within the landscape. Exploitation involves a more focused search within a promising region of the landscape seeking a solution that is the best available within that region, "good enough"--a local optimum. The most common exploitation strategy is "hill climbing"--once a promising region is found small trial and error steps are taken to find the best available solution in that region, the top of that hill. One of the difficulties with hill climbing is that we can get stuck on the hill--or a specific region of solutions--and not get off it to search other regions for solutions that might be better. The trade-off between exploration and exploitation is central to optimization and the difference is the size of steps through the search space or
the ability to "jump" from one solution region to others. That ability can be affected by "mutation" (random changes in the individuals searching), diversity of those individuals, immigration (bringing in new individuals), creativity, and chance encounters (with others outside the normal network. The difference between exploration and exploitation is the mutation rate, or size of steps through the search space. Simulated Annealing PP A powerful search strategy based on the metaphor of molecules cooling into a crystalline pattern after being heated. In a molten metal the molecules move chaotically, and as the metal cools they begin to find patterns of connectivity with neighboring molecules until they cool into a nice orderly pattern—an optimum. Simulated annealing takes the basic idea of hill climbing and adds to it a cooling schedule. A bitstring is modified by flipping randomly selected bits, and if the modified bitstring performs better, it replaces the original. If the modified bitstring performs worse, though, it can still be accepted if a probability test is passed. The probability threshold is a function of the system's "temperature," which decreases over time. The probability of accepting poorer problem solution decreases as the system cools; the effect of this is that the algorithm roams the search space widely in the early iterations, bouncing into and out of locally optimal regions. Later the algorithm will be focused on the more promising regions. Simulated annealing explores early in the experiment and exploits later—an approach that has been shown to be quite successful for many kinds of problems. --Singles bars & marriage partners http://www.taygeta.com/annealing/demo1.html
Ackley, E. S. (2000). Improving Dorm Room Assignments Using Simulated Annealing. MA thesis, University of New Mexico, Department of Computer Science.
The Dorm Assignment problem can readily be structured for simulated annealing:
• The dormitory system can be described concisely in terms of students and beds;
• The students can be rearranged in different beds randomly; • An objective function based on student preferences can be devised; • Through trial and error, and a bit of luck, an effective annealing schedule
can be found.
Assigning dorm rooms to 2,000 students with complex and interdependent preferences is a difficult problem. User dissatisfaction with an existing proprietary commercial matching system afforded an opportunity to consider fresh approaches. This project, begun August 1998, has focused on using simulated annealing to optimize dorm room assignments. The project goals are to improve usability and resource utilization of the assignment process, offering better, faster matches for happier students and staff. Having completed its third semester of production use at UNM, results show that the Dorm Assignment Optimizer (DAO) works well producing assignments far superior to the previous system, in a fraction of the time. Carley, Kathleen M. and D. M. Svoboda (1996). Modeling organizational adaptation as a simulated annealing process. Sociological Methods and Research 25(1): 138-168. Carley, K. M. (2000). Adaptive Organizations and Emergent Forms. Unpublished
paper, Carnegie Mellon Univ.) John P. Boyd, J.P. & Johnson, J. C. (1995). Finding N-culture consensus with simulated annealing : Mathematical Social Sciences, 30(1), 97-98 Optimizing with real numbers 77 Minds, species, Statistics and Optimization PP The optimization process is basically one of determining a “goodness of fit” of a neural network to some problem (or challenge) presented by the ecology and uses statistical concepts--such as minimizing the sum of squares (deviations of observations about some optimum). Some of these networks involves cause and effect relationships in the network (with IVs, mediating Vs, and DVs) and the process is basically equivalent to ANOVA or (usually) multiple regression (since usually the IVs are related). Thus research and statistics is central to what we do as a species--and all species do--and how we think. This is a tool for the "fuzzy logic" required by the real world (as opposed to abstract logic in philosophy). “Minds and species engage in something like optimization. … Nature doesn't necessarily look for the globally optimal solution to a problem (nature isn't looking for anything at all!); all that is required is that a solution be good enough ... . Both evolution and cognition require search of enormous spaces. As we have suggested, there can be a great number of search strategies. The social strategies of humans and evolutionary search are both population based, meaning that many solutions can be tested in parallel; in both cases, interactions among population members result in problem-solving intensity greater than the sum of individuals' solitary efforts.”
Chapter 3 -- On Our Nonexistence as Entities: The Social Organism This chapter considers the various zoom-angles that can be used to look at living and thinking things. Though we tend to think of ourselves as autonomous beings, we can be considered as macro-entities hosting multitudes of cellular or even sub-cellular guests, or as micro-entities inhabiting a planet that is alive. The chapter addresses some issues about social behavior. Why do animals live in groups? How do the social insects manage to build arches, organize cemeteries, stack wood chips? How do bird flocks and fish schools stay together? And what in the world could any of this have to do with human intelligence? (Hint: it has a lot to do with it.) Some interesting questions have had to be answered before robots could do anything on their own. Rodney Brooks’ subsumption architecture builds apparently goal-directed behavior out of modules. And what’s the difference between a simulated robot and an agent? Finally, Chapter 3 looks at computer programs that can converse with people. How do they do it? –Usually by exploiting the shallowness or mindlessness of most conversation.
Key concepts Levels of analysis and units of selectivity Gaia Inclusive fitness Sexual selection Memes Purpose and teleological causal forces Communication Mitochondria Optimization and minimizing error Chance encounters and diversity Slim mold Emergence of global effects Swarm definition Self-organization, symbolic communication, and stigmergy Optimization with ants "Our study ... of swarm intelligence and collection adaptation is motivated in part by the uninformed suspicion there is wisdom to be gained from it, and by the feeling that there is something about the disorderly interactions of dumb actors and their achievements that is just, well, fascinating. It seems that there is something profound and meaningful in these phenomena, something that transcends the compulsive rationality imposed by our intellectual tradition" (p. 109) [epiphany!]
People's thoughts as swarms Causal attribution, internal and external causes, equifinality and local causality--Heider, Some Situational Determinants of Causal Attribution--Fontaine. Chapter 4 – Evolutionary Computation Theory and Paradigms This chapter describes the four major computational paradigms that use evolutionary theory for problem solving in some detail. The fitness of potential problem solutions is calculated, and the survival of the fittest allows better solutions to reproduce. These powerful methods are known as the "second best way" to solve any problem. Chapter 5 – Humans – Actual, Imagined, and Implied This chapter starts off musing on language as a bottom-up phenomenon. The chapter goes on to reviews the downfall of behavioristic psychology and the rise of cognitivism – meanwhile, social psychology kept simmering in the background. Clearly there is a relationship between culture and mind, and a number of researchers have tried to write computer programs based on that relationship. As we review various paradigms, it becomes apparent that a lot of people think that culture must be similar to Darwinistic evolution. Are they the same? How are they different? Chapter 6 – Thinking is Social This chapter eases us into our own research on social models of optimization. The Adaptive Culture Model is based on Axelrod’s Culture Model – in fact it is exactly like it except for one little thing: individuals imitate their neighbors, not on the basis of similarity, but on the basis of their performance. If your neighbor has a better solution to the problem than you do, you try to be more like them. It is a very simple algorithm with big implications. Chapter 7- The Particle Swarm This chapter begins by suggesting that the same simple processes that underlie cultural adaptation can be incorporated into a computational paradigm. Multivariate decision making is reflected in a binary particle swarm. The performance of binary particle swarms is then evaluated on a number of benchmarks. The chapter then describes the real-valued particle swarm optimization paradigm. Individuals are depicted as points in a shared high-dimensional space. The influence of each individual’s successes and those of neighbors is similar to the binary version, but change is now portrayed as movement rather than probability. The chapter concludes with a description of the use of particle swarm optimization to find the weights in a simple neural network.
Chapter 8 – Variations and Comparisons This chapter is a somewhat more technical look at what various researchers have done with the basic particle swarm algorithm. We first look at the effects of the algorithm’s main parameters, and at a couple of techniques for improving performance. Are particle swarms actually just another kind of evolutionary algorithm? There are reasons to think so, and reasons not to. Considering the similarities and differences between evolution and culture can help us understand the algorithm and possible things to try with it. Chapter 9 – Applications This chapter reviews a few of the applications of particle swarm optimization. The use of particle swarm optimization to evolve artificial neural networks is presented first. Evolutionary computation techniques have most commonly been used to evolve neural network weights, but have sometimes been used to evolve neural network structure or the neural network learning algorithm. The strengths and weaknesses of these approaches are reviewed. The use of particle swarm optimization to replace the learning algorithm and evolve both the weights and structure of a neural network is described. An added benefit of this approach is that it makes scaling or normalization of input data unnecessary. The classification of the Iris Data Set is used to illustrate the approach. Although a feedforward neural network is used as the example, the methodology is valid for practically any type of network. Chapter 10 – Implications and Speculations This chapter reviews the implications of particle swarms for theorizing about psychology and computation. If social interaction provides the algorithm for optimizing minds, then what must that be like for the individual? Various social- and computer-science perspectives are brought to bear on the subject. Chapter 11 – And In Conclusion… This chapter looks back at some of the motifs that were woven through the narrative.
Conway's Game of Life From Wikipedia, the free encyclopedia.
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Gosper Glider Gun creating "gliders".
The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular automaton.
Contents [hide]
• 1 Origins • 2 Rules of Life • 3 Description • 4 The game • 5 Iteration • 6 Examples of patterns • 7 Algorithms • 8 Variations on Life • 9 Patterns
o 9.1 125/36 o 9.2 245/3 (245/36)
• 10 See also • 11 Bibliography • 12 External Article Links • 13 Patterns and Pattern Collections• 14 Life Program Links • 15 External Cellular Automata Links
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Origins Conway became interested in a problem in group theory proposed by mathematician John Leech having to do with the symmetry group of a particular dense packing of spheres in 24 dimensions. Conway found some remarkable
properties and published the results in 1968. Conway was also interested in a problem presented in the 1940s by renowned mathematician John von Neumann. Von Neumann tried to find a hypothetical machine that could build copies of itself and succeeded when he found a mathematical model for such a machine with very complicated rules on a cartesian grid. Conway tried to simplify von Neumann's ideas and eventually succeeded. Coupling his previous success with Leech's problem in group theory with his interest in von Neuman's ideas concerning self-replicating machines resulted in the Game of Life.
It made its first public appearance in the October 1970 issue of Scientific American, in Martin Gardner's "Mathematical Games" column. From a theoretical point of view, it is interesting because it has the power of a universal Turing machine: that is, anything that can be computed algorithmically can be computed within Conway's Game of Life. It has often been claimed that since 1970 more computer time world-wide has been devoted to the Game of Life than any other single activity. Gardner wrote:
"The game made Conway instantly famous, but it also opened up a whole new field of mathematical research, the field of cellular automata... Because of Life's analogies with the rise, fall and alterations of a society of living organisms, it belongs to a growing class of what are called "simulation games" - games that resemble real-life processes."
Ever since its publication, it has attracted much interest because of the surprising ways the patterns can evolve. Life is an example of emergence and self-organization. It is interesting for biologists, mathematicians, economists, philosophers and others to observe the way that complex patterns can emerge from the implementation of very simple rules.
Life has a number of recognised patterns which emerge from particular starting positions. Soon after publication several interesting patterns were discovered, including the ever-evolving R-pentomino (more commonly known as "F-pentomino" outside the game), the self-propelling "glider", and various "guns" which generate an endless stream of new patterns, all of which led to increased interest in the game. Its popularity was helped by the fact that it came into being just in time for a new generation of inexpensive minicomputers which were being released into the market, meaning that the game could be run for hours on these machines which were otherwise unused at night. In this respect it foreshadowed the later popularity of computer-generated fractals. For many aficionados Life was simply a programming challenge, a fun way to waste CPU cycles. For some, however, Life had more philosophical connotations. It developed a cult following through the 1970s and into the mid-1980s; current developments have gone so far as to create theoretic emulations of computer systems within the confines of a Life board.
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Rules of Life Conway chose his rules carefully, after a long period of experimentation, to meet three criteria:
1. There should be no initial pattern for which there is a simple proof that the population can grow without limit.
2. There should be initial patterns that apparently do grow without limit. 3. There should be simple initial patterns that grow and change for a
considerable period of time before coming to an end in the following possible ways:
1. Fading away completely (from overcrowding or from becoming too sparse)
2. Settling into a stable configuration that remains unchanged thereafter, or entering an oscillating phase in which they repeat an endless cycle of two or more periods.
In other words, the rules should be such as to make the behavior of the population both interesting and unpredictable.
The rules are simple and elegant:
1. Any live cell with fewer than two neighbors dies of loneliness. 2. Any live cell with more than three neighbors dies of crowding. 3. Any dead cell with exactly three neighbors comes to life. 4. Any live cell with two or three neighbors lives, unchanged, to the next
generation.
It is important to understand that all births and deaths occur simultaneously. Together they constitute a single generation or, we could call it, a "tick" in the complete "life history" of the initial configuration.
Conway's rules may be generalized so that instead of two states (live and dead) there are q states (where q >= 2). Taking q = 2 results in a cellular automaton whose rules are equivalent to Conway's. In this q-state Life one may find 'gliders', etc., as in Conway's Life, although they occur more rarely.
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Description The "game" is actually a zero-player game, meaning that its evolution is determined by its initial state, needing no input from human players. It runs on a grid of squares ("cells") which stretches to infinity in all directions. Each cell has eight "neighbours", which are the cells adjacent to it, including diagonally. Each
cell can be in one of two states: it is either "alive" or "dead". (The terms "on" and "off" are also used.) The state of the grid evolves in discrete time steps. The states of all of the cells at one time are taken into account to calculate the states of the cells one time step later. All of the cells are then updated simultaneously. The transitions depend only on the number of live neighbours (see the rules above).
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The game The basic idea of the "game" is to start with a simple configuration of living cells (organisms) which are placed on a 2D grid by various methods. This constitutes the first generation. Conway's "natural laws" for births, deaths and survivals (the four rules above) are then applied to the pattern and the next generation pattern is placed accordingly. Generation by generation the "player(s)" observe the various patterns that emerge.
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Iteration From an initial pattern of living cells on the grid, you will find, as the generations tick by, the population constantly undergoing unusual, sometimes beautiful and always unexpected, change. In a few cases the society eventually dies out (all living cells vanishing), although this may not happen until after a great many generations. Most initial patterns either reach stable figures - Conway calls them "still lifes" - that cannot change or patterns that oscillate forever. Patterns with no initial symmetry tend to become symmetrical. Once this happens the symmetry cannot be lost, although it may increase in richness.
Conway originally conjectured that no pattern can grow without a limit. Put another way, any configuration with a finite number of counters cannot grow beyond a finite upper limit to the number of counters on the field. This was probably the deepest and most difficult question posed by the game at the time. Conway offered a prize of $50 to the first person who could prove or disprove the conjecture before the end of 1970. One way to disprove it would be to discover patterns that keep adding counters to the field: A "gun" (a configuration that repeatedly shoots out moving objects such as the "glider") or a "puffer train" (a configuration that moves but leaves behind a trail of "smoke"). The prize was won in November of the same year by a team from M.I.T. The initial configuration (shown below) grows into such a gun, emitting the first glider on the 40th generation. The gun emits a new glider every 30th generation from then on.
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Examples of patterns
It has been suggested that Seeds (CA) be merged into this article or section. (Discuss)
The evolution and movement of a "glider".
There are all sorts of different patterns that occur in the Game of Life, including static patterns ("still lifes"), repeating patterns ("oscillators" - a superset of still lifes), and patterns that translate themselves across the board ("spaceships"). The simplest examples of these three classes are shown below, with live cells shown in black, and dead cells shown in white.
Block Boat Blinker Toad Glider LWSS
The "block" and "boat" are still lifes, the "blinker" and "toad" are oscillators, and the "glider" and "lightweight spaceship" ("LWSS") are spaceships which steadily march their way across the grid as time goes on.
Patterns called "Methuselahs" can evolve for long periods before repeating. "Diehard" is a pattern that eventually disappears after 130 generations, or steps. "Acorn" takes 5206 generations to generate 13 gliders then stabilises as many oscillators.
Diehard Acorn
In the game's original appearance in "Mathematical Games", Conway offered a cash prize for any patterns that grew indefinitely. The first of these was found by Bill Gosper in November 1970. They include "guns", which are stationary and shoot out gliders or other spaceships; "puffers", which move along leaving behind a trail of debris; and "rakes", which move and emit spaceships. Gosper also later discovered a pattern with a quadratic growth rate, called a "breeder", which worked by leaving behind a trail of guns. Since then, various complicated
constructions have been made, including glider logic gates, an adder, a prime number generator, and a unit cell which emulates the Game of Life at a much larger scale and slower pace.
The first glider gun found is still the smallest one known:
Gosper Glider Gun
Simpler patterns were later found that also have infinite growth. All three of the following patterns have infinite growth. The first two create one blocklaying switch engine each, while the third creates two. The first has only 10 live cells (which has been proven to be minimal). The second fits in a 5 x 5 square. The third is only 1 cell high:
It is possible for gliders to interact with other objects in interesting ways. For example, if two gliders are shot at a block in just the right way, the block will move closer to the source of the gliders. If three gliders are shot in just the right way, the block will move further away. This "sliding block memory" can be used to simulate a counter. It is possible to construct logic gates AND, OR and NOT using gliders. It is possible to build a pattern which acts like a finite state machine connected to two counters. This has the same computational power as a universal Turing machine (see counter for the proof), so the Game of Life is as powerful as any computer with unlimited memory: it is Turing complete. Furthermore, a pattern can contain a collection of guns that combine to construct new objects, including copies of the original pattern. A "universal constructor" can be built which contains a Turing complete computer, and which can build many types of complex objects, including more copies of itself. (Descriptions of these constructions are given in Winning Ways by Conway, Elwyn Berlekamp and Richard Guy.)
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Algorithms The earliest results in the Game of Life were obtained without the use of computers. The simplest still-lifes and oscillators were discovered while tracking the fates of various small starting configurations using graph paper, blackboards, physical game boards and pieces, and the like. During this early research, Conway discovered that the R-pentomino failed to stabilize in a small number of generations.
These discoveries inspired computer programmers over the world to write programs to track the evolution of Life patterns. Most of the early algorithms were similar. They represented Life patterns as two-dimensional arrays in computer memory. Typically two arrays are used, one to hold the current generation and one in which to calculate its successor. Often 0 and 1 represent dead and live cells, respectively. A double loop considers each element of the current array in turn, counting the live neighbors of each cell to decide whether the corresponding element of the successor array should be 0 or 1. At the end of this process, the contents of the successor array are moved to the current array, the successor array is cleared, and the current array is displayed.
A variety of minor enhancements to this basic scheme are possible, and there are many ways to save unnecessary computation. A cell that did not change at the last time step, and none of whose neighbors changed, is guaranteed not to change at the current time step as well, so a program that keeps track of which areas are active can save time by not updating the inactive zones.
In principle, the Life field is infinite, but computers have finite memory, and usually array sizes must be declared in advance. This leads to problems when the active area encroaches on the border of the array. Programmers have used several strategies to address these problems. The simplest strategy is simply to assume that every cell outside the array is dead. This is easy to program, but leads to inaccurate results when the active area crosses the boundary. A more sophisticated trick is to consider the left and right edges of the field to be stitched together, and the top and bottom edges also. The result is that active areas that move across a field edge reappear at the opposite edge. Inaccuracy can still result if the pattern grows too large, but at least there are no pathological edge effects. Techniques of dynamic storage allocation may also be used, creating ever-larger arrays to hold growing patterns. Alternatively, the programmer may abandon the notion of representing the Life field with a 2-dimensional array, and use a different data structure, like a vector of coordinate pairs representing live cells. This approach allows the pattern to move about the field unhindered, as long as the population does not exceed the size of the live-coordinate array. The drawback is that counting live neighbors becomes a search operation, slowing
down simulation speed. With more sophisticated data structures this problem can also be largely solved.
For exploring large patterns at great time depths, sophisticated algorithms like Hashlife may be useful.
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Hello all, Just a note that I am offering a Communication Seminar next spring on "Culture, Self-Organization and Swarm Intelligence." It is numbered as COM480 because I hope to have a dynamic mix of grad students and advanced undergrad students. Graduate students will be expected to carry some additional course responsibilities and it thus can count for graduate credit in our MA program. I will also approve it's use for applying 3 credits toward the 6 seminar credit requirement in our MA program. It will be offered M & W 10:30 to 11:45. A brief seminar description follows. Aloha, Gary
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Culture, Self-Organization and Swarm Intelligence This seminar examines current theories, research, and interventions associated with emerging "evolutionary" (as opposed to "rational") models of optimization strategies presented by work in biology, evolutionary programming, genetic algorithms, engineering, the social/behavioral sciences and business involving the related fields of self-organization in biological systems and swarm intelligence. Particular emphasis is on the implications of this work for culture and the role of communication in dealing with diversity in the completion of collaborative tasks. As a seminar, the expectation is that all participants will contribute actively to our learning relevant to this topic in both structured and unstructured ways. The material itself is diverse and challenging. We are going to "swarm" our topics to optimize our understanding of them and our ability to apply that understanding to our society and ourselves. Gary Fontaine, Ph.D. Professor and Graduate Chair School of Communications University of Hawaii Honolulu, HI 96822 USA www2.hawaii.edu/~fontaine/garyspag.html
Monday [added replys to “The game ...” "Robotroaches" Hello all, Here on a wet rainy Monday in Ahuimanu are a few more bits I’ve gathered over the last year or two relevant to our recent rumblings about at cyborgs here in the Inn and self-organizing on the sugarscape and blending into other “cultures:” WASHINGTON (AP) - “Tiny robots programmed to act like roaches were able to blend into cockroach society, according to researchers studying the collective behavior of insects. Cockroaches tend to self-organize into leaderless groups, seeming to reach consensus on where to rest together. For example, when provided two similar shelters, most of the group tended to gather under the same one. Hoping to learn more about this behavior, researchers led by Jose Halloy at the Free University of Brussels, Belgium, designed small robots programmed to act like a cockroach. The robots didn't look like the insects and at first the roaches fled from them, but after the scientists coated the robots with pheromones that made them smell like roaches the machines were accepted into the group, nesting together with the insects. Given a choice, roaches generally prefer a darker place and the robots were programmed to do the same. When given a choice of a darker or lighter shelter, 75 percent of the cockroaches and 85 percent of the robots gathered under the darker one. Then, to see if the robots had really become part of society and could influence group decisions, Halloy and colleagues programmed them to prefer shelters with more light. The result, the lighter shelter was preferred by the mixed group 61 percent of the time, while the cockroaches alone picked it just 27 percent of the time. On the other hand, in 39 percent of cases the robots, despite being programmed to prefer a lighter shelter, joined the cockroaches under the darker one.” [The findings were reported in a recent issue of the journal Science--http://www.sciencemag.org ,11/16/07 18:35 © Copyright The Associated Press.]
Watch who's on your Team!!! And Colin Nickerson of the Boston Globe (11/16/07) expands as follows (so, indeed, be wary of the robots among us!): “With Robotic Bugs, Larger Ethical Questions. Research reported in Science describes how European scientists were able to use tiny robots placed in a colony of laboratory cockroaches to manipulate the actions of the insects. The robots, using behavioral modification methods, were able to convince the real insects to follow them into bright areas, a significant achievement considering cockroaches are known for hiding in dark areas. The significance of the research is that even simple robots can significantly influence group behavior. Some scientists believe that it is inevitable that advances in robotics and technology will ultimately alter the fundamental relationship between humanity and technology, and many analysts say now is
the time to seriously consider the ethical implications of technological advances. In many Asian countries with highly advanced robotic research laws are being considered that would regulate how much independence robots should be given by programmers, and even what "rights" should be given to robots. One issue of particular interest is whether robots will be given the ability to make life-or-death decisions involving humans, for example in a hospital or battlefield. Only two months ago, an unmanned aircraft deployed by U.S. forces in Iraq made its first "kill." While the drone was remote controlled, the action highlights the possibility of robotic warriors capable of making their own decisions. "We are embarking on the process of creating the first intelligent species to share the earth with humans since the time of the Neanderthals," says renowned science fiction author Robert Sawyer, who wrote an essay that accompanied the report in Science. "We're racing past the era of robo-vacuum cleaners into someplace quite different and more complex." Or this one-- Bee Strategy Helps Servers Run More Sweetly (Megan McRainey,Georgia Institute of Technology (11/16/07). “Georgia Institute of Technology researchers have developed a communications system for Internet servers based on the dance-based communication system bees use to divide limited resources. The new computer system allows servers that would normally be reserved for a single task to move between tasks as needed, reducing the chances that a Web site will be overwhelmed and lock out potential users. The new system helped servers improve service by 4 percent to 25 percent in real Internet traffic tests. Because bees have a limited number of workers to send out to collect pollen, scout bees are sent to find lucrative spots. These scout bees return to the hive and perform a dance to tell other bees where to find the nectar. The forager bees then dance behind the scout until they learn the right steps. Forager bees continue to follow the scout bee's dance until the nectar runs out or they find a more attractive dance. The system allows the bees to seamlessly shift from one source to another without a leader or central command to slow the decision process. Most server systems are theoretically optimized for "normal" conditions, which frequently change due to human nature. If demand for one site surges, servers not assigned to that site may remain idle while users are put into a queue that forces them to wait for the server assigned to the site to become available. When the bee server system receives a request for a site, the system places an internal advertisement, the equivalent of the bee's dance, to attract any available servers. The ad's duration is determined by demand for the site and how much revenue the site's users may generate. The longer an ad remains active, the more power available servers send to serve the Web site request.” Or finally this BBC News report from India: “The deputy mayor of the Indian capital Delhi has died a day after being attacked by a horde of wild monkeys. SS Bajwa suffered serious head injuries when he fell from the first-floor terrace of his home on Saturday morning trying to fight off the monkeys. The city has long
struggled to counter its plague of monkeys, which invade government complexes and temples, snatch food and scare passers-by. The High Court ordered the city to find an answer to the problem last year. Monkeys attack Delhi politician . One approach has been to train bands of larger, more ferocious langur monkeys to go after the smaller groups of Rhesus macaques.” Another approach, of course, would be to enlist the help of some robotmonkies to nurture the immergence of a more benign monkey culture. Or maybe we could do something like that for our course here in the Inn! Aloha, Gary And Yikes--the "Numerati" take over IBM! (http://www.businessweek.com/magazine/content/08_36/b4098032904806.htm) Or try this ... “Napoleon Dynamite” http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?_r=2&scp=3&sq=If%20you%20liked&st=cse&oref=slogin&oref=slogin And we haven't even started talking about dear Google yet! Aloha, Gary Monday RobotBees Hello all, You thought I was kidding about the “Smart Dust” and “RobotRoaches” and the need to fumigate the Inn. Check this--hot off the press--
Bug-sized spies: US develops tiny flying robots http://www.thestate.com/technology/story/598066.html?RSS=business http://media.thestate.com/smedia/2008/11/21/15/Bug_Sized_Spies.sff.mi_embedded.prod_affiliate.74.jpg Also, just a quick note to let all know that--while I have tried to keep my intrusions to a minimum this week because I know you are all busy with the Handbooks due the end of this week (and other courses and lives)--I am always still hanging around here in the Inn through the week. Aloha, Gary
