Cognitive technologies: mapping the Internet governance debate

by Goran S. Milovanović

Introduction

Among the words that first come to mind when Internet governance (IG) is mentioned, complexity surely scores in the forerunners. But do we ever grasp the full complexity of such issues? Is it possible for an individual human mind ever to claim a full understanding of a process that encompasses thousands of actors, a plenitude of different positions, articulates an agenda of almost non‑stop ongoing meetings, conferences, forums, and negotiations, while addressing the interests of billions of Internet users? With the development of the Internet, the Information Society, and the Internet governance processes, the amount of information that demands effective processing in order for us to act rationally and in real time increases tremendously. Paradoxically, the Information Age, marked by the discovery of the possibility of digital computers in the first half of the twentieth century, demonstrated the shortcomings in processing capacities very quickly as it progressed. The availability of home computers and the Internet have been contributing to this paradox since the early 1990s: as the number of networked social actors grew, the governance

processes naturally faced increased demand for information processing and management. But this is not simply a question of how many raw processing power or how much memory storage we have at our disposal. The complexity of social processes that call for good governance, as well as the amount of communication that mediates the actions of the actors involved, increase up to a level where qualitatively different forms of management must come into play. One cannot understand them by simply looking at them, or listening to what everyone has to say: there are so many voices, and among billions of thoughts, ideas, concepts, and words, there are known limits to human cognition to be recognised.

The good news is, as the Information Age progresses, new technologies, founded upon the scientific attempts to mimic the cognitive functions of the human mind, are becoming increasingly available. Many of the computational tools that were only previously available to well‑funded research initiatives in cognitive science and artificial intelligence can nowadays run on average desktop computers and laptops. With increased trends of cloud computing and the parallel execution of thousands of lines of computationally demanding code, the application

This paper• provides a simple explanation of what cognitive technologies are.• gives an overview of the main idea of cognitive science (why human minds and computers could

be thought of as being essentially similar kinds of systems).• discusses in brief how developments in engineering and fundamental research interact to result

in cognitive technologies.• presents an example of applied cognitive science (text‑mining) in the mapping of the Internet

governance debate.

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of cognitive technologies in attempts to discover meaningful regularities in vast amounts of structured and unstructured data is now within reach. If the known advantages of computers over human minds – namely, the speed of processing that they exhibit in repetitive, well‑structured, daunting tasks performed over huge sets of data – can combine with at least some of the advantages of our natural minds over computers, what new frontiers are touched upon? Can computers do more than beat the best of our chess players? Can they help us to better manage the complexity of societal consequences that have resulted from our own discovery and the introduction of digital technologies to human societies? How can cognitive technologies help us analyse and manage global governance processes such as IG? What are their limits and how will they contribute to societal changes themselves? These are the questions that we address in this short paper, tackling the idea of cognitive technology and providing an illustrative example of their application in the mapping of the IG debate.

Box 1: Cognitive technologies

• The Internet links people; networked computers are merely mediators.

• By linking people globally, the Internet has created a network of human minds – systems that are a priori more complex than digital computers themselves.

• The networked society exchanges a vast amount of information that could not have been transmitted before the inception of the Internet: management and governance issues become critical.

• New forms of governance introduced: global IG.

• New forms of information processing introduced: cognitive technologies. They result from the application of cognitive science that studies both natural and artifi cial minds.

• Contemporary cognitive technologies present an attempt to mimic some of the cognitive functions of the human mind.

• Increasing raw processing power (cloud computing, parallelisation, massive memory storage) nowadays enables for a widespread application of cognitive technologies.

• How do they help and what are their limits?

The main idea: mind as a machine

For obvious reasons, many theoretical discussions and introductions to IG begin with an overview of the history of the Internet. For reasons less obvious, many discussions about the Internet and the Information Society tend to suppress the historical presentation of an idea that is clearly more important than the very idea of the Internet. The idea is characteristic of the cognitive psychology and cognitive science of the second half of the twentieth century, and it states – to put it in a nutshell – that human minds and digital computers possibly share many important, even essential properties, and that this similarity in their design – which, as many believe, goes beyond pure analogy – opens a set of prospects towards the development of artifi cial intelligence, which might prove to be the most important technological development in the future history of human kind if achieved. From a practical point of view, and given the current state of the technological development, the most important consequence is that at least some of the cognitive functions of the human mind can be mimicked by digital computers. The fi eld of computational cognitive psychology, where behavioural data collected from human participants in experimental settings are modelled mathematically, increasingly contributes to our understanding that the human mind acts in perception, judgment, decision‑making, problem‑solving, language comprehension, and other activities as if it is governed by a set of natural principles that can be eff ectively simulated on digital computers. Again, even if the human mind is essentially diff erent from a modern digital computer, these fi ndings open a way towards the simulation of human cognitive functions and their enhancement (given that digital computers are able to perform many simple computational tasks with effi ciency which is orders of magnitudes above the effi ciency of natural minds).

An overview of cornerstones in the historical development of cognitive science is given in Appendix I. The prelude to the history of cognitive science belongs to the pre World War II epoch, when a generation of brilliant mathematicians and philosophers, certainly best represented by an ingenious British mathematician Alan Mathison Turing (1912–1954), paved the way towards the discovery of the limits formalisation in logic and mathematics

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in general. By formalisation we mean the expression of any idea in a strictly defi ned, unambiguous language, precisely enough that no two interpretants could possibly argue over its meaning. The concept of formalisation is important: any problem that is encoded by a set of transformations over sequences of symbols – in other words, by a set of sentences in a precise, exact, and unambiguous language – is said to be formalised. The question of whether there is meaning to human life, thus, can probably be never formalised. The question of whether there is a certain way for the white to win a chess game given its initial advantage of having the fi rst move can be formalised, since chess is a game that receives a straightforward formal description through its well‑defi ned, exact rules. Turing was among those to discover a way of expressing any problem that can be formalised at all in the form of a computer program for abstract computational machinery known as the Universal Turing Machine (UCM). By providing the defi nition for his abstract computer, he was able to show how any mathematical reasoning – and all mathematical reasoning takes place in strictly formalised languages – can be essentially understood as a form of computation. Unlike computation in a narrow sense, where its meaning usually refers to basic arithmetic operations with numbers only, this broad sense of computation encompasses all precisely defi ned operations over symbols and sets of symbols in some predefi ned alphabet. The alphabet is used to describe the problem, while the instructions to the Turing Machine control its behaviour which essentially presents no more than the translation of sets of symbols from their initial form to some other form, with one of the possible forms of transformation being discovered and recognised as a solution to the given problem – the moment when the machine stops working. More important, from Turing’s discovery, it followed that formal reasoning in logic and mathematics can be performed mechanically, i.e., an automated device could be constructed that computes any computable function at all. The road towards the development of digital computers was thus open. But even more important, following Turing’s analyses of mechanical reasoning, the question of whether the human mind is simply a biological incarnation of universal computation – a complex universal digital computer, instantiated by biological evolution instead being a product of design processes, and implemented in carbon‑based organic matter instead of silicon – was posed. The idea that human intelligence

shares the same essential properties as Turing’s mechanised system of universal computation proved to be the major driving force in the development of post World War II cognitive psychology. For the fi rst time in history, mankind not only developed the means of advancing artifi cial forms of thinking, but instantiated the fi rst theoretical idea that saw the human mind as a natural, mechanical system whose abstract structure is at least, in a sense, analogous to some well‑studied mathematical description. A way for the naturalisation of psychology was fi nally opened, and cognitive science, as the study of natural and artifi cial minds, was born.

Roughly speaking, three important phases in the development of its mainstream can be recognised during the course of the twentieth century. The fi rst important phase in the development of cognitive science was marked by a clear recognition that, at least in principle, the human mind could operate on principles that are exactly the same as those that govern universal computation. Newell and Simon’s Physical Systems Hypothesis [1] provides probably the most important theoretical contribution to this fi rst, pioneering phase. Attempts to design universal problem solvers and design computers that successfully play chess were characteristic of the fi rst phase. The ability to produce and understand natural language was recognised as a major characteristic of an artifi cially intelligent system. An essential critique of this fi rst phase in the historical development of cognitive science was provided by the philosopher Hubert Dreyfus in his classic What Computers Can’t Do in 1972. [2] The second phase, starting approximately in the 1970s and gaining momentum during the whole 1980s and 1990s, was characterised by an emphasis on the problems of learning, the restoration of importance of some of the pre World War II principles of behaviouristic psychology, the realisation that well‑defi ned formal problems such as chess are not really representative of the problems that human minds are really good at solving, and the exploitation of a class of computational models of cognitive functions known as neural networks. The results of this second phase, marked mainly by a theoretical movement of connectionism, showed how sets of strictly defi ned, explicit rules, almost certainly miss describing adequately the highly fl exible, adaptive nature of the human mind. [3a,3b] The third phase is rooted in the 1990s, when many cognitive scientists began to understand that human minds essentially operate on variables of uncertain

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value, with incomplete information, and in uncertain environments. Sometimes referred to as the probabilistic turn in cognitive science, [4] the important conclusion of this latest phase in the development of cognitive science is that the language of probability theory, used instead of (or in conjunction with) the language of formal logic, provides the most natural way to describe the operation of the human cognitive system. The widespread application of decision theory, describing the human mind as a biological organ that essentially evolved in order to perform the function of choice under risk and uncertainty, is characteristic of the most recent developments in this third, contemporary phase in the history of cognitive science. [5]

Box 2. The rise of cognitive science

In summary:• Fundamental insights in twentieth century

logic and mathematics enabled a fi rst attempt at a naturalistic theory of human intelligence.

• Alan Turing’s seminal contribution to the theory of computation enabled a direct parallel between the design of artifi cially and naturally intelligent systems.

• This theory, in its mainstream form, sees no essential diff erences between the structure of the human mind and the structure of digital computers, both viewed at the most abstract level of their design.

• Diff erent theoretical ideas and mathematical theories were used to formalise the functioning of the mind during the second half of the twentieth century. The ideas of physical symbol systems, neutral networks, and probability and decision theory, played the most prominent roles in the development of cognitive science.

The machine as a mind: applied cognition

As widely acknowledged, humanity still did not achieve the goal of developing true artifi cial intelligence. What, then, is applied cognition? At the current stage of development, applied cognitive science encompasses the application of mostly partial solutions to partial cognitive problems. For example, we cannot build software

that reads Jorge Luis Borges’ collected short stories and then produces a critical analysis from a viewpoint of some specifi c school of literary critique. One would say not many human beings can actually do that. But we can’t accomplish even simpler tasks; with the general rule that as cognitive tasks get more general, the harder it gets to simulate them. But, what we can do, for example, is to feed the software with a large collection of texts from diff erent authors, let it search through it, recognise the most familiar words and patterns of word usage, and then successfully predict the authorship of a previously unknown text. We can teach computers to recognise some visual objects by learning with feedback from their descriptions in terms of simpler visual features, and we are getting good at making them recognise faces and photography. We cannot ask a computer to act creatively in the way that humans do, but we can make them prove complicated mathematical theorems that would call for years of mathematical work by hand, and even produce aesthetically pleasing visual patterns and music by sampling, resampling, and adding random but not completely irregular noise to initial sound patterns.

In cognitive science, engineers learn from psychologists, and vice versa, mathematical models, developed initially to solve purely practical problems, are imported in psychological theories of cognitive functions. The goals of the study that cognitive engineers and psychologists pursue are only somewhat diff erent. While the latter addresses mainly the functioning of natural minds, the former does not have to constrain a solution to some cognitive problem by imposing on it the limits of the human mind and realistic neurophysiology of the brain. Essentially, the direction of the arrow usually goes from mathematicians and engineers towards psychologists: the ideas proposed in the fi eld of artifi cial intelligence (AI) are tested only after having them dressed in a suit of empirical psychological theory. However, engineers and mathematicians in AI discover their ideas by observing and refl ecting on the only known truly intelligent system, namely, the real, natural, human mind.

Many computational methods were thus fi rst discovered in the fi eld of AI before they were tried out as explanations of the functioning of the human mind. To begin with, the idea of physical symbol systems, provided by Newell and Simon in the early formulation of cognitive science, presents a direct interpretation of a symbolic

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theory of computation initially proposed by Turing and the mathematicians in the fi rst half of the twentieth century. Neural networks, which present a class of computational models that can learn to respond to complex external stimuli in a fl exible and adaptive way, were clearly motivated by the empirical study of learning in humans and animals. However, they were fi rst proposed as an idea in the fi eld of artifi cial intelligence, and then only later applied in human cognitive psychology. Bayesian networks, known also as causal (graphical) models[6], represent structured probabilistic machinery that deal effi ciently with learning, prediction, and inference tasks, and were again fi rst proposed in AI before heavily infl uencing the most recent developments in psychology. Decision and game theory, to provide an exception, were initially developed and refl ected on in pure mathematics and mathematical economics, before being imported into the arena of empirical psychology, were they still represent both a focal subject of experimental research and a mathematical modelling toolkit.

The current situation in applying the known principles and methods of cognitive science can be described as eclectic. In applications to real‑world problems, and not necessarily to describe truthfully the functioning of the human mind, algorithms developed on the behalf of cognitive scientists do not need to obey any ‘theoretical purity’. Many principles discovered in empirical psychology, for example reinforcement learning, are applied without necessary applying them in exactly the same way as it is thought that they operate in natural learning systems.

As already noted, it’s uncertain whether applied cognition will ever produce any AI that will fully resemble the natural mind. A powerful analogy is proposed: for example, people rarely admit that the human kind has never understood natural fl ying in birds or insects, in spite of the fact that we have and use artifi cial fl ying of airplanes and helicopters. The equations that would correctly describe the natural, dynamic, biomechanical systems that fl y are simply too complicated and, in general, they cannot be analytically solved even if they can be described. But we have invented artifi cial fl ying by refl ecting on the principles of the fl ight of birds, without ever having a complete scientifi c understanding it. Maybe AI will follow the same path: we may have useful, practical, and powerful cognitive applications, even without ever understanding the functioning of the human mind in totality.

The main goal of current cognitive technologies, the products of applied cognitive science, is to help natural human minds to better understand very complex cognitive problems – those that would be hard to comprehend by our mental functions solely – and to increase the speed and amount of processing that some cognitive tasks require. For example, studying thousands of text documents in order to describe, at least roughly, what are the main themes that are discussed in them, can be automated to a degree to help human beings get the big picture without actually reading through all of them.

Box 3. Applied cognition

• Cognitive engineers and cognitive psychologists learn from each other. The former refl ect on natural minds and build algorithms that solve certain classes of cognitive problems, which leads directly to applications, while the latter test the proposed models experimentally to determine whether they describe the workings of the human mind adequately.

• Many principles of cognitive psychology are applied to real-world problems without necessary mimicking the corresponding faculties of the human mind exactly. We discover something, than change it to suit our present purpose.

• We provide partial solutions only, since global human cognitive functioning is still too diffi cult to describe. However, even partial solutions that are nowadays available skyrocket what computers could have done only decades ago.

• Contemporary cognitive technologies focus mainly on reducing the complexity of some cognitive tasks that would be hard to perform by relying on our natural cognitive functions only.

Example: applying text-mining to map the IG debate

The NETmundial Multistakeholder Statement of São Paulo1 – the fi nal outcome document of NETmundial (22, 23 April 2014), the Global Multistakeholder Meeting on the Future of IG – resulted from a political process of immense complexity. Numerous forms of inputs, various

1 http://netmundial.br/netmundial‑multistakeholder‑statement/

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expertise, several preformed bodies, a mass of individuals and organisations representing diff erent stakeholders, all interfaced both online and in situ, through a complex timeline of the NETmundial process, to result in this document. On 3 April, the NETmundial Secretariat prepared the fi rst draft, previously processing more than 180 content contributions. The fi nal document resulted following the negotiations in São Paulo, based on the second draft that was itself based on incorporating numerous suggestions made in comments to the fi rst draft. The multistakeholder process of document drafting introduced in its production is already seen by many as the future common ingredient of global governance processes in general. By the complexity of the IG debate alone, one could have anticipated that more complex forms of negotiations, decision‑shaping, and crowdsourced document production will naturally emerge. As the complexity of the processes under analysis increases, the complexity of tools used to conduct the analyses must increase also. At the present point of its development, DiploFoundation’s Text‑Analytics Framework (DTAF) operates on the Internet Governance Forum (IGF) Text Corpus, a collection of all available session, workshop, and panel transcripts from the IGF 2006–2014, encompassing more than 600 documents and utterances contributed on behalf of hundreds of speakers. By any standards in the fi eld of text-mining – an area of applied cognitive science which focuses on statistical analyses of patterns of words that occur in natural language – both the NETmundial collection of content contributions and the IGF Text Corpus present rather small datasets. The analyses of text corpora that encompass tens of thousands of documents are rather common. Imagine incorporating all websites, social media, newspaper and journal articles on IG, in order to perform a full‑scale monitoring of the discourse of the IG debate, and you’re already there.

Obviously, the cognitive task of mapping the IG debate represented even only by two text corpora that we discuss here, is highly demanding. It is questionable whether a single policy analyst or social scientist would manage to comprehend the full complexity of the IG discourse in several years of dedicated work. Here we illustrate the application of text‑mining, which is a typical cognitive technology used nowadays, to the discovery of useful, structured information in large collections of texts. We will focus our attention on the NETmundial corpus

of content contributions and ask the following question: What are the most important themes, or topics, that have appeared in this set of more than 180 contributions, including the NETmundial Multistakeholder Statement of São Paulo? In order to answer this question, we fi rst need to hypothesise a model of how the NETmundial discourse was produced. We rely on a fairly well‑studied and frequently applied model in text‑mining, known by its rather technical name of Latent Dirichlet Allocation (LDA, see Methodology section in Appendix II. [7,8,9]). In LDA, it is assumed that each word (or phrase) in some particular discourse is produced from a set of underlying topics with some initially unknown probability. Thus, each topic is defi ned as a probability distribution across the words and phrases that appear in the documents. It is also assumed that each document in the text corpus is produced from a mixture of topics, each of them weighted diff erently in proportion to their contribution to the generation of the words that comprise the document. Additional assumptions must be made about the initial distribution of topics across documents. All these assumptions are assembled in a graphical model that describes the relationships between the words, documents, and latent topics. One normally runs a number of LDA models that encompass diff erent number of topics and rely on the statistical properties of the obtained solutions to recognise which one provides the best explanation for the structure of the text corpus under analysis. In the case of the NETmundial corpus of content contributions, an LDA model with seven topics was selected. Appendix II presents fi fteen most probable words generated by each of the seven underlying topics. By inspecting which words are most characteristic in each of the topics discovered in this collection of texts, we were able to provide meaningful interpretations2 of the topics. We fi nd that NETmundial content contributions were mainly focused on questions of (1) human rights, (2) multistakeholderism, (3) global governance mechanism for ICANN, (4) information security, (5) IANA oversight, (6) capacity building, and (7) development (see Table A‑2.1 in Appendix II).

In order to help a human policy analyst in their research on the NETmundial, for example, we could determine the contribution of each of these seven topics to each document from the

2 I wish to thank Mr Vladimir Radunović of DiploFoundation for his help in the interpretation of the topics obtained from the LDA model of the NETmundial content contributions.

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collection of content contributions, so that the analyst interested in just some aspects of this complex process could select only the most relevant documents. As an illustration, Figure A‑2.1 in Appendix II presents the distributions of topics found in the content contributions of two important stakeholders in the IG arena, civil society and government. It is easily read from the displays that the representatives of the organisations of civil society strongly emphasised human rights (Topic 1 in our model) in their contributions, while representatives of national governments focused more on IANA oversight (Topic 5) and development issues (Topic 7).

Figure A‑2.2 in Annex II presents the structure of similarities between the most important words in the human rights topic (Topic 1, Table A‑2.1 in Annex II). We fi rst selected only the content contributions made on behalf of civil society organisations. Then we used the probability distributions of words across topics and the distribution of topic weights across the documents to compute the similarities between all relevant words. Since similarity computed in this way is represented in a high‑dimensional space and thus not suitable for visualisation, we have decided to use the graph represented in Figure A‑2.2. Each node in Figure A‑2.2 represents a word, and each word receives exactly three arrows. These arrows originate at nodes that represent those words that are found to be among the three most similar words to the target word. Each word is an origin of as many links as there are words in whose set of the three most similar words it is found. Thus we can use graph representation to assess the similarities in the patterns of word usage across diff erent collections of documents. The lower display in Figure A‑2.2 presents the similarity structure in the human rights topic extracted from governmental content contributions to NETmundial only. By comparing the two graphs, we can see that only slight diff erences appear, in spite of the fact that the importance of the human rights topic is diff erent in the content contributions of these two stakeholders. Thus, they seem to understand the conceptual realm of human rights in a similar way, but tend to accentuate it diff erently in the statements of their respective positions.

Conclusions that stream from our cognitive analysis of the NETmundial content contributions could have been brought by a person who did not actually read any of these documents at all. The analysis does rely on some built‑in human

expert knowledge, but once set, it can produce this and similar results in a fully automated manner. While it is not advisable to use this and similar methods instead of a real, careful study of the relevant documents, their power in improving on the work of skilled, thoroughly educated scholars and professionals should be emphasised.

Concluding remarks

However far we are from the ideal of true artifi cial intelligence, and given that the defi nition of what true artifi cial intelligence might be is not very clear in itself, cognitive technologies that have emerged after more than 60 years of study of the human mind as a natural system are nowadays powerful enough to provide meaningful application and valuable insight. With the increasing trends of big data, numerous scientists involved in the development of more powerful algorithms and even faster computers, cloud computing, and means for massive data storage, even very hard cognitive problems will become addressable in the near future. The planet, our ecosystem, now almost completely covered by the Internet, will introduce an additional layer of cognitive computation, making information search, retrieval, data mining, and visualisation omnipresent in our media environments.

A prophecy to end this paper with: not only will this layer of cognitive computation bring about more effi cient methods of information management and extend our personal cognitive capacities, it will itself introduce additional questions and complications to the existing IG debate. Networks intermixed with human minds and narrowly defi ned artifi cial intelligences will soon begin to present the major units of representing interests and ideas, and their future political signifi cance should not be underestimated now when their development is still in its infancy. They will grow fast, as fast as the fi eld of cognitive science did.

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Bibliography

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[2] Dreyfus H (1972) What computers can’t do. New York: MIT Press, ISBN 0‑06‑090613‑8

[3a] Rumelhart DE, McClelland JL and the PDP Research Group (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. Cambridge, MA: MIT Press.

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[6] Pearl J (2000) Causality. Models, Reasoning and Inference. Cambridge: Cambridge University Press.

[7] Blei DM, Ng AY, Jordan MI (2003) Laff erty J ed. Latent Dirichlet Allocation. Journal of Machine Learning Research 3(4–5), 993–1022. doi:10.1162/jmlr.2003.3.4‑5.993

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Appendix I

Timeline of cognitive science

Year Selected developments

1936 Turing publishes On Computable Numbers, with an Application to the Entscheidungsproblem. Emil Post achieves similar results independently of Turing. The idea that (almost) all formal reasoning in mathematics can be understood as a form of computation becomes clear.

1945 The Von Neumann Architecture, employed in virtually all computer systems in use nowadays, is presented.

1950 Turing publishes Computing machinery and intelligence, introducing what is nowadays known as the Turing Test for artifi cial intelligence.

1956 • George Miller discusses the constraints on human short‑term memory in computational terms.

• Noam Chomsky introduces the Chomsky Hierarchy of formal grammars, enabling the computer modeling of linguistic problems.

• Allen Newell and Herbert Simon publish a work on the Logic Theorist, mimicking the problem solving skills of human beings; the fi rst AI program.

1957 Frank Rosenblatt invents the Perceptron, an early neural network algorithm for supervised classifi cation. The critique of the Perceptron published by Marvin Minsky and Seymour Papert in 1969 is frequently thought of as responsible for delaying the connectionist revolution in cognitive science.

1972 Stephen Grossberg starts publishing results on neural networks capable of modeling various important cognitive functions.

1979 James J. Gibson publishes The Ecological Approach to Visual Perception.

1982 David Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information makes a strong case for computational models of biological vision and introduces the commonly used levels of cognitive analysis (computational, algorithmic/representational, and physical).

1986 Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vols 1 and 2, are published, edited by David Rumelhart, Jay McClelland, and the PDP Research Group. The onset of the connectionism (the term was fi rst used by David Hebb in the 1940s). Neural networks are considered as powerful models to capture the fl exible, adaptive nature of human cognitive functions.

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Year Selected developments

1990s • Probabilistic turn: the understanding slowly develops, in many scientifi c centres and the work of many cognitive scientists, that the language of probability theory provides the most suitable means of describing cognitive phenomena. Cognitive systems control the behaviour of organisms that have only incomplete information about uncertain environments to which they need to adapt.

• The Bayesian revolution: most probabilistic models of cognition expressed in mathematical models relying on the application of the Bayes theorem and Bayesian analysis. Latent Dirichlet Allocation (used in the example in this paper) is a typical example of Bayesian analysis.

• A methodological revolution is introduced by Pearl’s study of causal (graphical) models (also known as Bayesian networks).

• John Anderson’s methodology of rational analysis.

1992 Francisco J. Varela, Evan T. Thompson, and Eleanor Rosch publish The Embodied Mind: Cognitive Science and Human Experience, formulating another theoretical alternative to classical symbolic cognitive science.

2000s • Decision‑theoretic models of cognition. Neuroeconomics: the human brain as a decision‑making organ. The understanding of importance of risk and value in describing cognitive phenomena begins to develop.

• Geoff rey Hinton and others introduce deep learning: a powerful learning method for neural networks partially based on ideas that already went under discussion in the early 1990s and 1980s.

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Appendix II

Topic model of the content contributions to the NETmundial

Methodology. A terminological model of the IG discourse was fi rst developed by DiploFoundation’s IG experts. This terminological model encompasses almost 5000 IG‑specifi c words and phrases. The text corpus of NETmundial content contributions in this analysis encompasses 182 documents. The corpus was pre‑processed and automatically tagged for the presence of the IG‑specifi c words and phrases. The resulting document‑term matrix, describing the use frequencies of IG specifi c terms across 182 available documents, was modelled by Latent Dirichlet Allocation (LDA), a statistical model that enables for the recognition of semantic topics (i.e., thematic units) that accounts for the frequency distribution in the given document‑term matrix. A single topic comprises all IG‑specifi c terms; the topics diff er by the probability they assign to each IG‑specifi c term. The model selection procedures proceeded as follows. We split the text corpus into two halves, by randomly assigning documents to the training and the test set. We fi t the LDA models ranging from two to twenty topics to the training set and then compute the perplexity (an information‑theoretic, statistical measure of badness‑of‑fi t) of the fi tted models for the test set. We select the best model as the one with the lowest perplexity. Since the text corpus is rather small, we repeated this procedure 400 times and looked at the distribution of the number of topics from the best‑fi tting LDA models across all iterations. This procedure pointed towards a model encompassing seven topics. We then fi tted the LDA with seven topics to the whole NETmundial corpus of content contributions. Table A‑2.1 presents the most probable words per topics. The original VEM algorithm was used to estimate the LDA model.

Table A-2.1. Topics in the NETmundial Text Corpus. The columns represent the topics recovered by the application of LDA to the NETmundial content contributions. The words are enlisted by their probability of being generated by each topic.

Topic 1.Human Rights

Topic 2. Multi‑

stakeholderism

Topic 3. Global governance

mechanism for ICANN

Topic 4. Information

security

Topic 5. IANA

oversight

Topic 6. Capacity building

Topic 7. Development

right IG internet internet ICANN curriculum internet

human rights stakeholder global security IANA technology IG

principle internet governance service organisation analysis global

cyberspace principle ICANN data function research development

state process need cyber operation education principle

information discuss technical network account blog open

internet issue role country process online governance

protection participation system need review association participation

access ecosystem issue control policy similarity continue

communication need IG information DNS term stakeholder

surveillance role local nation board product access

law multistakeholder principle policy GAC content model

respect governance level eff ective multistakeholder integration organisation

international NETmundial country trade model innovative innovative

charter address state user government public economic

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Figure A-2.1. The comparison of civil society and government content contributions to NETmundial. We assessed the probabilities with which each of the seven topics from the LDA model of the NETmundial content contributions determine the contents of the documents, averaged across all documents per stakeholder, normalised and expressed the contribution of each topic in %.

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Figure A-2.2. The conceptual structures of the topic of human rights (Topic 1 in the LDA model of NETmundial content contributions) for civil society and government contributions. The graphs represent the 3‑neighbourhoods of the 15 most important words in the topic of human rights (Topic 1 in the LDA model). Each node represents a word and has exactly three arrows pointed at it: the nodes from which these arrows originate represent the words found to be among the three words most similarly used to a word that receives the links.

Government

Civil Society

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About the author

Goran S. Milovanović is a cognitive scientist who studies behavioural decision theory, perception of risk and probability, statistical learning theory, and psychological semantics. He has studied mathematics, philosophy, and psychology at the University of Belgrade, and graduated from the Department of Psychology. He began his PhD studies at the Doctoral Program in Cognition and Perception, Department of Psychology, New York University, USA, while defending a doctoral thesis entitled Rationality of Cognition: A Meta-Theoretical and Methodological Analysis of Formal Cognitive Theories at the Faculty of Philosophy, University of Belgrade, in 2013. Goran has a classic academic training in experimental psychology, but his current work focuses mainly on the development of mathematical models of cognition, and the theory and methodology of behavioural sciences.

He organised and managed the fi rst research on Internet usage and attitudes towards information technologies in Serbia and the region of SE Europe, while managing the research programme of the Center for Research on Information Technologies (CePIT) of the Belgrade Open School (2002–2005), the foundation of which he initiated and supported. He edited and co‑authored several books on Internet Behaviour, attitudes towards the Internet, and the development of the Information Society. He managed several research projects on Internet Governance in cooperation with DiploFoundation (2002–2014) and also works as an independent consultant in applied cognitive science and da