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MIS 585Special Topics in MIS:Agent-Based Modeling
2015/2016 Fall
Chapter 2Models, Modeling Cycle and
theODD Protocol
Outline
1. What is Model2. Modeling CycleODD Protocol
1. What is a Model?
• A model is a purposeful simpoified representation of a real system
• In science:– How thinks work – Explain patterns that are observed– Predict systems bevaior in response to some
change
• Social systems– Too complex or slowly changing to be
experimentally studied
Models
• Formulate a model– design its assumptions and algorithms
• Different ways of simplfing real systems– Which aspect to include , which to ignore
• Purpase– The questions to be answered is the filter
• all aspects of the real system– irrelevant or insufficiently important– to answer the question are filtered out
Searching Mushrooms in a Forest
• Is there a best strategy for searching mushrooms?
• observation:– mushrooms in clusters
• An intuitive strategy:– scanning an area in wide sweeps– upon finding a mushroom turning to
smaller scale sweeps– as mushrroms in clusters
Searching Mushrooms in a Forest
• What is large, small sweeps? and • How long to search in smaller sweeps?• Humans searching
– pizzas, jobs, low price goods, peace with neighbors
• mushroom hunter– sensing radius is limited– must move to detect new mushrooms
Why develop a model for the problem
• try different search strategies– not obvious with textual models
• Purpose:– what search strategy maximizes
musrooms found in a given time• Ignore trees and vegitables, soil type • Include: musrooms are distributed as
clusters
Simplified hunter
• mushroom hunter– moving point– having a sensing radius– track of
• how many mushrooms found• how much time passed since last mushroom
fouınd
Formulate a model
• clusters of items (mushrooms)• If the agent (hunter) finds an item• smaller-scale movement• If a critical time passes since last
item found• swithes back to more streight
movement• so as to find new clusters of items
Why model
• Here processes and behavior is simple• in general what factors are important
– regarding the question addresed by the model
– not possible So– formulate – implement in computers– analize
• rigorously explore consequences of assuptions
First Formulation
• First formulation of the model– Preliminary understanding about how
the system works– Proceses structure
• Based on– Empirical knowledge system’s behavior– Theory– Earlier models with the same purpose– Intiution or imagination
Good model
• Assumptions at first experimental• Test whether they are appropriate
and useful• Need a criteria – model is a good
representation of the real system– Patterns and regularities
Stock Market Example
• Example: Stock market model– Volatility and trends of stock prices
volumes,…
• First version – Too simple - lack of prcecesses
structure– Inconsistant -
2. The Modeling Cycle
• When developing a model– Series of tasks – systematically– consequences of simplfiing assumptions
• Iterating through the taasks– First models are – Too simple , too complex or wrong
questions
The Modeling Cycle
• Modeling cycle:Grimm and Reilsbeck (2005)– Formulate the question– Assamble hypothesis– Choose model structure– Implement the model– Analyze the model– Communicate the model
Formulate the Question
• Clear research question• Primary compass or filter for designing
the model• clear focus• Experimentat may be reformulated• E.g.: for MH Model
– what strategies maximizes the rate of fining items if they are distributed in clusters
Assamble Hypothesis
• Whether an element or prosses is an esential for addresing the modeling questions - an hypothesis – True or false
• Modeling:– Build a model with working hypothsis– Test – useful and sufficient– Explanation, prediction - observed
phenomena
Assamble Hypothesis (cont.)
• Hypothesis of the conceptual model– Verbally graphically– Based on Theory and and experience
• Theory provides a framework to persive a system
• Experience– Knowlede who use the sysem
Assamble Hypothesis (cont.)
• Formulate many hypothesis• What process and structures are
essentiaal• Start top-down
– What factors have a strong influence on the phenomena
– Are these factors independent or interacting– Are they affected by ohter important factors
Assamble Hypothesis (cont.)
• Influence diagrams, flow charts• Based on
– Existing knowledge, simplifications
–
Basic Strategy
• Start with simple as simple as possible
• even you are sure that some factors are important
• Gilbert: analogy null hypothesis in satatistics – agaainst my claim
• Implement as soon as possible
Guidelines
• Mere realizm is a poor guideline for modeling– must be guided by a problem or question
about a real system – not by just the system itself
• Constraints are esential to modeling– on information understanding time
• Modeling is hardwired into our brains– we use powerful modeling heuristics to solve
problems
Heuristics for Modeling
• pleusable way or reasonalble approach that has often proved to be useful
• Rephrase the problem• Draw simple diagrams• Inagine that you are indide the system• Try to idendify esential variables• identify assumptions• Use salami tactics
E.g.: MH Model
• Esential process• swithcing between large scale
movementgs and small scale searching
• Depending on how long it has been since the hunter has found an item.
Choose scale, state variable, processes, parameters
• Variables derscribing environment• Not all charcteristics
– Relevant wtih the question
• Examples– Position (location)Age, gender,
education, income, state of– mind ,…
Choose scal, state variable, processes, parameters
• Example• Parameter being constant• Exchange rate between dolar and
euro– Constrant for travelers, not for traders
Choose scale, state variable, processes, parameters
• Scale– Time and spatial
• Grain: smalest slica of time or space• Extent: total time or area covered by
the model• The gain or time spen: step over
which we ignore variation in variables
Choose scale, state variable, processes, parameters
• Choose scales, entities, state variables processes and parameters
• Transfering hypothesis into equations rules
• Describing dynamics of entities
Choose scale, state variable, processes, parameters
• Variables – derscribing state of thr system
• The essential process – cause change of these variables
• In ABM – interacting individuals
• agent-agent, agent-environment
– Variables – individual– parameters
E.g.: HM Model
• Space items are in and hunter moves• Objects - agents
– one hunter and items to be searched• hunter
– state variables• time• how many items found• time last found
– bevaior: search strategy
Implementation
• Mathematics or cpmputer programs • To translate verbal conceptual model
into annimated objects• Implemented model has its own
dynamics and life
Implementation
• Assumption may be wong or incomplete but impolementation is right– Allows to explore the consequences of
assumption
• Start with the simplest - null model• Set parameters , initial values of
variables
Analysis
• Analysing the model and learing with the aid of the model
• Most time consuming and demanding part• Not just implementing agents and run the
model• What agents behavior can explain
important characteristics of real systems• When to stop iterations of the model
cycle?
E.g.: HM Model
• Try different search algorithms– with different parameters
• to see which search algorithm – strategy is the best
Communication of the model
• Communicate model and results to – Scientific community– Managers
• Observations, experiments, findings and insights are only when
• Others repreduce the finings independently and get the same insights
Example of a Model
• Consumer behavior model:– How friends influence consumer choices
of indivduals• Buy according to their preferences• what one buys influeces her friends
decisions– interraction
Example of a Model
• verbal• mathematical
– theoretical model– Emprical : statistical equations
• estimated from real data based on questioners
• simulation models of customer behavior– ABMS – interractions, learning,
formation of networks
Theoretical Models
• Analytical models• Restrictive assumptions
– Rationality of agent– Representative agents– Equilibrium
• Contradicts with observations– Labaratory experiments about humman
subjects
Theoretical Models
• as precision get higher explanatory power lower– closed form solutions
• Relaxation of assumptions– geting a closed form solution is
impossible
Emprical Models
• Historically mathematical differential equations
• Or emprical models represente by algberic or difference equations whose parameters are to be estimated
Simulation Models
• Simulation • ABMS:
– Represent indiduals as autonomous units, their interractions with each other and environment
– Chracteristics – variables– and behavior
• Variables – state of the whole system
How ABM differs
• Units agents differ in terms of resourses, size history
• Adaptive behavior: adjust themselfs looking current state which may hold information about past as well. other agent environment or by forming expectations about future states
• Emergence: ABM across-level models
Skills
• A new language for thiking about or derscribing models
• Software• Strategy for model development and
analysis
3. Summery and Conclustions
• ABM relatively new – way of looking old as well as new
problems– complex (adaptive) systems– improve understanding
• What is modeling• What ABM brings• Model development cycle
Ant
• An ant forgang food• Model:
– an abstracted describtion of a process, object event
Ants
• manipulability– textual – hard to manipulatfe– E.g.: what if all ants have the same
behavior
• A computational model– takes inputs, manipulates by algorithms
and produces outputs
• Model implementation– from textual to computer code
Ants
• an ant – agent– properties– behavior
Creating the Ant Foraging Model
The ODD Protocol
• Originaly for decribing ABMs or IBMs• Useful for formulating ABNs as well.• Wha kind of thigs should be in AMB?• What bahavior agents should have?• What outputs are needed_• A way of think and describe about
ABModeling
The ODD Protocol
• ODD Owverwiew Design concepts and Details
• Seven elements• Three elements overwiew what the
odel is about• One design element • Three elements deteild description of
the model complete
Purpose
• Statement of the question or problem addresed by the model
• What system we are modeling_• What we are trying to learn?
Entities, state variable scales
• What are its entities– The kind of thinks represented in the model
• What variables are used to characterize them
• ABMs• One or more types of agents
Entities, state variable scales
• The environment in which agents live and interract– Local units or patches – Global environment
• State variables: how the model specify their state at any time
• An agent’sd state – properties or attributes– Size, age, saving, opinion, memory
• Behavioral strategy:– Searching behavior– Bidding behavior– Learning
• Some state variables constant– Gender location of immobile agents– Varies among agents but stay constant
through out the life of the agent
• Space : grids networks • Global envionment: variables change
over time usually not in space– Temperature tx rate
• Golbal Variables:• Usually not affected by agents • Exogenuous, • Provideded as data input or coming
from submodels
Process overwiew and and Scheduleing
• Structure v.s. Dynamics• Process that change the state
variables of model entities• Describes the behavior or dynamics
of odel entity• Dercribe each process with a name
– Selling buying biding influensing
Observer Processes
• Only processes that are not liked to one of the model entities
• Modeler – creator of the model– Observe and record
• What the model entities do• Why and when they do it• Display model’s status ona graphical
display• Write statistical summaries to output files
Model’s Schedule
• The order in which processes are executed• Action: model’sd scedule is a sequence of
actions– What model entities– What processes– What order
• Some simple • For many ABMs schedule is complex
– Use a pseudo code
Design Concepts
• How a model implements a set of basic concepts
• standardized way of thinking important and unique characteristics of ABM
• What outcomes emerge from what characteristics of agents and their environment
• Basic principles• Emergence• Adaptation• Objectives• Learning• Prediction• Interraction• Stochasticity• Collectives• Observation
Initialization
• Number of agents• Provide values for state variables of
entities or environment
Initialization
• Model results depends on initial conditions– Price txx rate
• Not depends on inigtial conditions– Comming from distributions– Run the model until memory of the
initial state is forgoten the effect of initial valus disapear
– Replicate teh model
Input Data
• Environmental variables– usually change over time– policy variables
• price promotions advertising expenditures
– pyjrt rcsöğşrd• temperatukre
• not parameters • they may change over time as well
• not initial values
Submodels
• deiteld description o fprosseses• not only agorithms or pseudo code• but
– why we formulate the submodel– what literature is is based on– assumptions– where to get parameter values– how to test or calibrate the model