Cognitive technologies: mapping the Internet governance debate by Goran S. Milovanović

This paper • provides a simple explanation of what cognitive technologies are. • gives an overview of the main idea of (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.

Introduction processes naturally faced increased demand for information processing and management. But this is not simply a question of how many raw Among the words that first come to mind processing power or how much memory storage when Internet governance (IG) is mentioned, we have at our disposal. The complexity of social complexity surely scores in the forerunners. processes that call for good governance, as well But do we ever grasp the full complexity of such as the amount of communication that mediates issues? Is it possible for an individual human the actions of the actors involved, increase up mind ever to claim a full understanding of a to a level where qualitatively different forms of process that encompasses thousands of actors, management must come into play. One cannot a plenitude of different positions, articulates an understand them by simply looking at them, or agenda of almost non‑stop ongoing meetings, listening to what everyone has to say: there are conferences, forums, and negotiations, while so many voices, and among billions of thoughts, addressing the interests of billions of Internet ideas, concepts, and words, there are known users? With the development of the Internet, limits to human to be recognised. the Information Society, and the Internet governance processes, the amount of information The good news is, as the Information Age that demands effective processing in order for progresses, new technologies, founded upon the us to act rationally and in real time increases scientific attempts to mimic the cognitive functions tremendously. Paradoxically, the Information of the human mind, are becoming increasingly Age, marked by the discovery of the possibility of available. Many of the computational tools that digital computers in the first half of the twentieth were only previously available to well‑funded century, demonstrated the shortcomings research initiatives in cognitive science and in processing capacities very quickly as it can nowadays run on progressed. The availability of home computers average desktop computers and laptops. With and the Internet have been contributing to this increased trends of cloud computing and the paradox since the early 1990s: as the number of parallel execution of thousands of lines of networked social actors grew, the governance computationally demanding code, the application of cognitive technologies in attempts to discover The main idea: mind as a machine meaningful regularities in vast amounts of structured and unstructured data is now within reach. If the known advantages of computers For obvious reasons, many theoretical over human minds – namely, the speed of discussions and introductions to IG begin with processing that they exhibit in repetitive, an overview of the history of the Internet. For well‑structured, daunting tasks performed reasons less obvious, many discussions about over huge sets of data – can combine with at the Internet and the Information Society tend to least some of the advantages of our natural suppress the historical presentation of an idea minds over computers, what new frontiers that is clearly more important than the very idea are touched upon? Can computers do more of the Internet. The idea is characteristic of the than beat the best of our chess players? Can cognitive and cognitive science of they help us to better manage the complexity the second half of the twentieth century, and of societal consequences that have resulted it states – to put it in a nutshell – that human from our own discovery and the introduction minds and digital computers possibly share many of digital technologies to human societies? How important, even essential properties, and that can cognitive technologies help us analyse and this similarity in their design – which, as many manage global governance processes such believe, goes beyond pure analogy – opens a as IG? What are their limits and how will they set of prospects towards the development of contribute to societal changes themselves? These artifi cial intelligence,which might prove to be are the questions that we address in this short the most important technological development paper, tackling the idea of cognitive technology in the future history of human kind if achieved. and providing an illustrative example of their From a practical point of view, and given the application in the mapping of the IG debate. current state of the technological development, the most important consequence is that at least some of the cognitive functions of the human Box 1: Cognitive technologies mind can be mimicked by digital computers. The fi eld of computational , • The Internet links people; networked where behavioural data collected from computers are merely mediators. human participants in experimental settings • By linking people globally, the Internet are modelled mathematically, increasingly has created a network of human minds – contributes to our understanding that the systems that are a priori more complex human mind acts in perception, judgment, than digital computers themselves. decision‑making, problem‑solving, language • The networked society exchanges a vast comprehension, and other activities as if it is amount of information that could not have governed by a set of natural principles that can been transmitted before the inception of be eff ectively simulated on digital computers. the Internet: management and governance Again, even if the human mind is essentially issues become critical. diff erent from a modern digital computer, these • New forms of governance introduced: fi ndings open a way towards the simulation of human cognitive functions and their global IG. enhancement (given that digital computers are • New forms of information processing able to perform many simple computational introduced: cognitive technologies. They tasks with effi ciency which is orders of result from the application of cognitive magnitudes above the effi ciency of natural science that studies both natural and minds). artifi cial minds. • Contemporary cognitive technologies An overview of cornerstones in the historical present an attempt to mimic some of the development of cognitive science is given cognitive functions of the human mind. in Appendix I. The prelude to the history of • Increasing raw processing power (cloud cognitive science belongs to the pre World computing, parallelisation, massive War II epoch, when a generation of brilliant memory storage) nowadays enables for mathematicians and philosophers, certainly a widespread application of cognitive best represented by an ingenious British technologies. mathematician Alan Mathison Turing (1912–1954), • How do they help and what are their limits? paved the way towards the discovery of the limits formalisation in logic and mathematics

2 in general. By formalisation we mean the shares the same essential properties as Turing’s expression of any idea in a strictly defi ned, mechanised system of universal computation unambiguous language, precisely enough that proved to be the major driving force in the no two interpretants could possibly argue over development of post World War II cognitive its meaning. The concept of formalisation is psychology. For the fi rst time in history, mankind important: any problem that is encoded by a set not only developed the means of advancing of transformations over sequences of symbols – artifi cial forms of thinking, but instantiated the in other words, by a set of sentences in a precise, fi rst theoretical idea that saw the human mind exact, and unambiguous language – is said to as a natural, mechanical system whose abstract be formalised. The question of whether there structure is at least, in a sense, analogous to is meaning to human life, thus, can probably some well‑studied mathematical description. be never formalised. The question of whether A way for the naturalisation of psychology was there is a certain way for the white to win a fi nally opened, and cognitive science, as the chess game given its initial advantage of having study of natural and artifi cial minds, was born. the fi rst move can be formalised, since chess is a game that receives a straightforward formal Roughly speaking, three important phases in description through its well‑defi ned, exact rules. the development of its mainstream can be Turing was among those to discover a way of recognised during the course of the twentieth expressing any problem that can be formalised century. The fi rst important phase in the at all in the form of a computer program for development of cognitive science was marked abstract computational machinery known as the by a clear recognition that, at least in principle, Universal Turing Machine (UCM). By providing the human mind could operate on principles the defi nition for his abstract computer, he that are exactly the same as those that govern was able to show how any mathematical universal computation. Newell and Simon’s reasoning – and all mathematical reasoning Physical Systems Hypothesis [1] provides probably takes place in strictly formalised languages the most important theoretical contribution to – can be essentially understood as a form of this fi rst, pioneering phase. Attempts to design computation. Unlike computation in a narrow universal problem solvers and design computers sense, where its meaning usually refers to basic that successfully play chess were characteristic arithmetic operations with numbers only, this of the fi rst phase. The ability to produce and broad sense of computation encompasses all understand natural language was recognised as precisely defi ned operations over symbols and a major characteristic of an artifi cially intelligent sets of symbols in some predefi ned alphabet. system. An essential critique of this fi rst phase in The alphabet is used to describe the problem, the historical development of cognitive science while the instructions to the Turing Machine was provided by the philosopher Hubert Dreyfus control its behaviour which essentially presents in his classic What Computers Can’t Do in 1972. no more than the translation of sets of symbols [2] The second phase, starting approximately from their initial form to some other form, with in the 1970s and gaining momentum during one of the possible forms of transformation the whole 1980s and 1990s, was characterised being discovered and recognised as a solution by an emphasis on the problems of learning, to the given problem – the moment when the restoration of importance of some of the the machine stops working. More important, pre World War II principles of behaviouristic from Turing’s discovery, it followed that formal psychology, the realisation that well‑defi ned reasoning in logic and mathematics can be formal problems such as chess are not really performed mechanically, i.e., an automated representative of the problems that human device could be constructed that computes any minds are really good at solving, and the computable function at all. The road towards the exploitation of a class of computational models development of digital computers was thus open. of cognitive functions known as neural networks. But even more important, following Turing’s The results of this second phase, marked mainly analyses of mechanical reasoning, the question by a theoretical movement of , of whether the human mind is simply a biological showed how sets of strictly defi ned, explicit incarnation of universal computation – a complex rules, almost certainly miss describing universal digital computer, instantiated by adequately the highly fl exible, adaptive nature of biological evolution instead being a product the human mind. [3a,3b] The third phase is rooted of design processes, and implemented in in the 1990s, when many cognitive scientists carbon‑based organic matter instead of silicon began to understand that human minds – was posed. The idea that human intelligence essentially operate on variables of uncertain

Geneva Internet Conference 3 value, with incomplete information, and in that reads Jorge Luis Borges’ collected short uncertain environments. Sometimes referred stories and then produces a critical analysis from to as the probabilistic turn in cognitive science, [4] a viewpoint of some specifi c school of literary the important conclusion of this latest phase in critique. One would say not many human beings the development of cognitive science is that the can actually do that. But we can’t accomplish language of probability theory, used instead of even simpler tasks; with the general rule that (or in conjunction with) the language of formal as cognitive tasks get more general, the harder logic, provides the most natural way to describe it gets to simulate them. But, what we can do, the operation of the human cognitive system. for example, is to feed the software with a large The widespread application of , collection of texts from diff erent authors, let it describing the human mind as a biological organ search through it, recognise the most familiar that essentially evolved in order to perform the words and patterns of word usage, and then function of choice under risk and uncertainty, is successfully predict the authorship of a previously characteristic of the most recent developments unknown text. We can teach computers to in this third, contemporary phase in the history recognise some visual objects by learning with of cognitive science. [5] feedback from their descriptions in terms of simpler visual features, and we are getting good at making them recognise faces and photography. Box 2. The rise of cognitive science We cannot ask a computer to act creatively in the way that humans do, but we can make them prove In summary: complicated mathematical theorems that would • Fundamental insights in twentieth century call for years of mathematical work by hand, logic and mathematics enabled a fi rst and even produce aesthetically pleasing visual attempt at a naturalistic theory of human patterns and music by sampling, resampling, and intelligence. adding random but not completely irregular noise • Alan Turing’s seminal contribution to the to initial sound patterns. theory of computation enabled a direct parallel between the design of artifi cially In cognitive science, engineers learn from and naturally intelligent systems. psychologists, and vice versa, mathematical models, developed initially to solve purely • This theory, in its mainstream form, sees practical problems, are imported in psychological no essential diff erences between the theories of cognitive functions. The goals of the structure of the human mind and the study that cognitive engineers and psychologists structure of digital computers, both viewed pursue are only somewhat diff erent. While at the most abstract level of their design. the latter addresses mainly the functioning of • Diff erent theoretical ideas and natural minds, the former does not have to mathematical theories were used to constrain a solution to some cognitive problem formalise the functioning of the mind by imposing on it the limits of the human mind during the second half of the twentieth and realistic neurophysiology of the brain. century. The ideas of physical symbol Essentially, the direction of the arrow usually systems, neutral networks, and probability goes from mathematicians and engineers and decision theory, played the most towards psychologists: the ideas proposed in the prominent roles in the development of fi eld of artifi cial intelligence (AI) are tested only cognitive science. after having them dressed in a suit of empirical psychological theory. However, engineers and mathematicians in AI discover their ideas by The machine as a mind: applied observing and refl ecting on the only known truly cognition intelligent system, namely, the real, natural, human mind.

As widely acknowledged, humanity still did not Many computational methods were thus fi rst achieve the goal of developing true artifi cial discovered in the fi eld of AI before they were intelligence. What, then, is applied cognition? tried out as explanations of the functioning of the At the current stage of development, applied human mind. To begin with, the idea of physical cognitive science encompasses the application symbol systems, provided by Newell and Simon of mostly partial solutions to partial cognitive in the early formulation of cognitive science, problems. For example, we cannot build software presents a direct interpretation of a symbolic

4 theory of computation initially proposed by The main goal of current cognitive technologies, Turing and the mathematicians in the fi rst half of the products of applied cognitive science, is to the twentieth century. Neural networks, which help natural human minds to better understand present a class of computational models that very complex cognitive problems – those that can learn to respond to complex external stimuli would be hard to comprehend by our mental in a fl exible and adaptive way, were clearly functions solely – and to increase the speed and motivated by the empirical study of learning amount of processing that some cognitive tasks in humans and animals. However, they were require. For example, studying thousands of text fi rst proposed as an idea in the fi eld of artifi cial documents in order to describe, at least roughly, intelligence, and then only later applied in what are the main themes that are discussed human cognitive psychology. Bayesian networks, in them, can be automated to a degree to help known also as causal (graphical) models[6], human beings get the big picture without actually represent structured probabilistic machinery reading through all of them. that deal effi ciently with learning, prediction, and inference tasks, and were again fi rst proposed Box 3. Applied cognition in AI before heavily infl uencing the most recent developments in psychology. Decision and game • Cognitive engineers and cognitive theory, to provide an exception, were initially psychologists learn from each other. The developed and refl ected on in pure mathematics former refl ect on natural minds and build and mathematical economics, before being algorithms that solve certain classes of imported into the arena of empirical psychology, cognitive problems, which leads directly were they still represent both a focal subject to applications, while the latter test the of experimental research and a mathematical proposed models experimentally to modelling toolkit. determine whether they describe the workings of the human mind adequately. The current situation in applying the known • Many principles of cognitive psychology principles and methods of cognitive science are applied to real-world problems without can be described as eclectic. In applications to necessary mimicking the corresponding real‑world problems, and not necessarily to faculties of the human mind exactly. We describe truthfully the functioning of the human discover something, than change it to suit mind, algorithms developed on the behalf of our present purpose. cognitive scientists do not need to obey any • We provide partial solutions only, since ‘theoretical purity’. Many principles discovered in global human cognitive functioning is empirical psychology, for example reinforcement still too diffi cult to describe. However, learning, are applied without necessary applying even partial solutions that are nowadays them in exactly the same way as it is thought that available skyrocket what computers could they operate in natural learning systems. have done only decades ago. As already noted, it’s uncertain whether applied • Contemporary cognitive technologies cognition will ever produce any AI that will fully focus mainly on reducing the complexity of resemble the natural mind. A powerful analogy some cognitive tasks that would be hard to is proposed: for example, people rarely admit perform by relying on our natural cognitive that the human kind has never understood functions only. natural fl ying in birds or insects, in spite of the fact that we have and use artifi cial fl ying of Example: applying text-mining to map airplanes and helicopters. The equations that would correctly describe the natural, dynamic, the IG debate biomechanical systems that fl y are simply too complicated and, in general, they cannot be The NETmundial Multistakeholder Statement analytically solved even if they can be described. of São Paulo1 – the fi nal outcome document But we have invented artifi cial fl ying by refl ecting of NETmundial (22, 23 April 2014), the Global on the principles of the fl ight of birds, without Multistakeholder Meeting on the Future of IG ever having a complete scientifi c understanding – resulted from a political process of immense it. Maybe AI will follow the same path: we may complexity. Numerous forms of inputs, various have useful, practical, and powerful cognitive applications, even without ever understanding 1 the functioning of the human mind in totality. http://netmundial.br/netmundial‑multistakeholder‑ statement/

Geneva Internet Conference 5 expertise, several preformed bodies, a mass of content contributions and ask the following of individuals and organisations representing question: What are the most important themes, diff erent stakeholders, all interfaced both or topics, that have appeared in this set of more online and in situ, through a complex timeline than 180 contributions, including the NETmundial of the NETmundial process, to result in Multistakeholder Statement of São Paulo? In this document. On 3 April, the NETmundial order to answer this question, we fi rst need to Secretariat prepared the fi rst draft, previously hypothesise a model of how the NETmundial processing more than 180 content contributions. discourse was produced. We rely on a fairly The fi nal document resulted following the well‑studied and frequently applied model negotiations in São Paulo, based on the second in text‑mining, known by its rather technical draft that was itself based on incorporating name of Latent Dirichlet Allocation (LDA, see numerous suggestions made in comments to Methodology section in Appendix II. [7,8,9]). In the fi rst draft. The multistakeholder process of LDA, it is assumed that each word (or phrase) document drafting introduced in its production in some particular discourse is produced from is already seen by many as the future common a set of underlying topics with some initially ingredient of global governance processes in unknown probability. Thus, each topic is defi ned general. By the complexity of the IG debate as a probability distribution across the words alone, one could have anticipated that more and phrases that appear in the documents. It complex forms of negotiations, decision‑shaping, is also assumed that each document in the text and crowdsourced document production corpus is produced from a mixture of topics, will naturally emerge. As the complexity each of them weighted diff erently in proportion of the processes under analysis increases, to their contribution to the generation of the the complexity of tools used to conduct the words that comprise the document. Additional analyses must increase also. At the present assumptions must be made about the initial point of its development, DiploFoundation’s distribution of topics across documents. All Text‑Analytics Framework (DTAF) operates these assumptions are assembled in a graphical on the Internet Governance Forum (IGF) Text model that describes the relationships between Corpus, a collection of all available session, the words, documents, and latent topics. One workshop, and panel transcripts from the normally runs a number of LDA models that IGF 2006–2014, encompassing more than encompass diff erent number of topics and rely 600 documents and utterances contributed on the statistical properties of the obtained on behalf of hundreds of speakers. By any solutions to recognise which one provides standards in the fi eld of text-mining – an area the best explanation for the structure of the of applied cognitive science which focuses on text corpus under analysis. In the case of the statistical analyses of patterns of words that NETmundial corpus of content contributions, occur in natural language – both the NETmundial an LDA model with seven topics was selected. collection of content contributions and the IGF Appendix II presents fi fteen most probable Text Corpus present rather small datasets. The words generated by each of the seven underlying analyses of text corpora that encompass tens of topics. By inspecting which words are most thousands of documents are rather common. characteristic in each of the topics discovered in Imagine incorporating all websites, social media, this collection of texts, we were able to provide newspaper and journal articles on IG, in order to meaningful interpretations2 of the topics. We perform a full‑scale monitoring of the discourse fi nd that NETmundial content contributions were of the IG debate, and you’re already there. mainly focused on questions of (1) human rights, (2) multistakeholderism, (3) global governance Obviously, the cognitive task of mapping mechanism for ICANN, (4) information security, the IG debate represented even only by two (5) IANA oversight, (6) capacity building, and (7) text corpora that we discuss here, is highly development (see Table A‑2.1 in Appendix II). demanding. It is questionable whether a single policy analyst or social scientist would manage In order to help a human policy analyst in their to comprehend the full complexity of the IG research on the NETmundial, for example, we discourse in several years of dedicated work. could determine the contribution of each of Here we illustrate the application of text‑mining, these seven topics to each document from the which is a typical cognitive technology used nowadays, to the discovery of useful, structured 2 I wish to thank Mr Vladimir Radunović of DiploFoundation information in large collections of texts. We will for his help in the interpretation of the topics obtained focus our attention on the NETmundial corpus from the LDA model of the NETmundial content contributions.

6 collection of content contributions, so that the expert knowledge, but once set, it can produce analyst interested in just some aspects of this this and similar results in a fully automated complex process could select only the most manner. While it is not advisable to use this relevant documents. As an illustration, Figure and similar methods instead of a real, careful A‑2.1 in Appendix II presents the distributions study of the relevant documents, their power of topics found in the content contributions of in improving on the work of skilled, thoroughly two important stakeholders in the IG arena, educated scholars and professionals should be civil society and government. It is easily read emphasised. 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 Concluding remarks governments focused more on IANA oversight (Topic 5) and development issues (Topic 7). However far we are from the ideal of true Figure A‑2.2 in Annex II presents the structure artifi cial intelligence, and given that the defi nition of similarities between the most important of what true artifi cial intelligence might be is words in the human rights topic (Topic 1, not very clear in itself, cognitive technologies Table A‑2.1 in Annex II). We fi rst selected only that have emerged after more than 60 years of the content contributions made on behalf of study of the human mind as a natural system civil society organisations. Then we used the are nowadays powerful enough to provide probability distributions of words across topics meaningful application and valuable insight. and the distribution of topic weights across the With the increasing trends of big data, numerous documents to compute the similarities between scientists involved in the development of more all relevant words. Since similarity computed in powerful algorithms and even faster computers, this way is represented in a high‑dimensional cloud computing, and means for massive data space and thus not suitable for visualisation, storage, even very hard cognitive problems will we have decided to use the graph represented become addressable in the near future. The in Figure A‑2.2. Each node in Figure A‑2.2 planet, our ecosystem, now almost completely represents a word, and each word receives covered by the Internet, will introduce an exactly three arrows. These arrows originate additional layer of cognitive computation, making at nodes that represent those words that are information search, retrieval, data mining, found to be among the three most similar words and visualisation omnipresent in our media to the target word. Each word is an origin of as environments. many links as there are words in whose set of the three most similar words it is found. Thus A prophecy to end this paper with: not only we can use graph representation to assess the will this layer of cognitive computation bring similarities in the patterns of word usage across about more effi cient methods of information diff erent collections of documents. The lower management and extend our personal cognitive display in Figure A‑2.2 presents the similarity capacities, it will itself introduce additional structure in the human rights topic extracted questions and complications to the existing IG from governmental content contributions to debate. Networks intermixed with human minds NETmundial only. By comparing the two graphs, and narrowly defi ned artifi cial intelligences we can see that only slight diff erences appear, will soon begin to present the major units of in spite of the fact that the importance of the representing interests and ideas, and their human rights topic is diff erent in the content future political signifi cance should not be contributions of these two stakeholders. Thus, underestimated now when their development is they seem to understand the conceptual realm still in its infancy. They will grow fast, as fast as of human rights in a similar way, but tend to the fi eld of cognitive science did. 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

Geneva Internet Conference 7 Bibliography

[1] Newell A and Simon HA (1976) Computer Science as Empirical Inquiry: Symbols and Search., Communications of the ACM, 19(3), 113–126, doi:10.1145/360018.360022

[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.

[3b] McClelland JL, Rumelhart DE and the PDP Research Group (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models. Cambridge, MA: MIT Press.

[4] Oaksford M and Chater N (2009) Précis of Bayesian rationality: The probabilistic approach to human reasoning. Behav Brain Sci 32(1), 69–84. doi: 10.1017/S0140525X09000284

[5] Glimcher P (2003) Decisions, Uncertainty, and the Brain. The Science of Neuroeconomics. Cambridge, MA: MIT Press.

[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 Research 3(4–5), 993–1022. doi:10.1162/jmlr.2003.3.4‑5.993

[8] Griffi thsTL, Steyvers M and Tenenbaum JB (2007) Topics in semantic representation. Psychological Review 114, 211 244. http://dx.doi.org/10.1037/0033‑295X.114.2.211

[9] Grün B and Hornik K (2011) topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software 40(3). Available at http://www.jstatsoft.org/v40/i13

8 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.

Geneva Internet Conference 9 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.

10 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. Topic 2. Topic 3. Topic 4. Topic 5. Topic 6. Topic 7. Human Rights Multi‑ Global governance Information IANA Capacity Development stakeholderism mechanism for security oversight building ICANN 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

Geneva Internet Conference 11 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 %.

12 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.

Civil Society

Government

Geneva Internet Conference 13 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

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