1. Social Networks: from communication to solidarity (an interdisciplinary approach)
Fundación Sierra-Pambley, León (Spain)
León, September 13-15
Participating and Anticipating Actors and Agent Networks. Social Computing
Gordana Dodig Crnkovic
Professor of Computer Science
Mälardalen University,
School of Innovation, Design and Engineering

2. Mälardalen University, Sweden

3. Abstract
Computing have changed modern society in very profound ways – our means of communication with other people, our everyday habits, entertainment, work, transportation, schools, hospitals, … computing is becoming omnipresent, and essential for human society. As participants in this major technological and cultural change, we want to be able to understand ongoing processes and anticipate future possibilities. That is the goal of social computing. Moreover, computing as a method provides means for this study. There are two different approaches to social computing – from the social side, focusing on the important influence of computers on society and from the computational side – focusing on new type of computation that is performed by huge groups of agents (actors) exchanging information in networks. This lecture puts emphasis on technological aspects of social computing and its relation to general models of computing as information processing.
Keywords: Actors and Agent Networks. Social Computing. Info-computationalism. Information and computation.

4. Human brain is biological information processor - network of neurons processing information
Human Connectome Project

5. Human groups are information processing networks – knowledge generators
Google Knowledge Graph

6. Sciences are created through scientists knowledge networks
Atlas of Science

7. Conceptual Basis: Network Modells
Human protein interaction network
Protein network in yeast cells
Social network
Human connectome

8. Learning and knowledge
Learning and knowledge
A child is born with nervous system and a brain as a network of neurons which provide ccapacity to interact and learn.
Hebbs learning theory: "cells that fire toghether, wire togher”

9. World as information for an agent
From:

10. Classical sciences as information & knowledge networks
Natural sciences
(Physics,
Chemistry,
Biology, …) 2
Social sciences (Economy,
Sociology,
Antropology, …) 3
Humanities
(Philosophy, History, …) 4
Logic & Mathematics 1
Knowledge as Wissenschaft 5
Culture 6 10

11. Computing as Lingua Franca
Natural sciences
(Physics,
Chemistry,
Biology, …) 2
Social sciences (Economy,
Sociology,
Antropology, …) 3
Humanities
(Philosophy, History, …) 4
Logic & Mathematics 1
C O M P U T I N G
Knowledge as Wissenschaft 5
Culture 6 11

12. Information – Knowledge Networks
We are part of a “COGNITIVE REVOLUTION”
And it is important to understand how processes of information exchange and knowledge generation function.

13. Knowledge generated by individuals is shared in groups and society
Bilden från:

15. Networks of networks of information and knowledge – show complexity
In a complex system, what we see is dependent on where we are and what sort of interaction is used to study the system.
Computational study of complex systems: generative models They answer the question: How does the complexity arize? Evolution is the most well known generative mechanism for generating increasingly complex systems (organisms).

16. Info-computational framework: connecting informational structures and processes from quantum physics to living organisms and societies
Nature is described as a complex informational structure for a cognizing agent.
Computation is information dynamics (information processing) constrained and governed by the laws of physics on the fundamental level.
Information is the difference in one information structure that makes a difference in another information structure.

17. Computing Nature
The basic idea of computing nature is that all processes taking place in physical world can be described as computational processes – from the world of quantum mechanics to living organisms, their societies and ecologies. Emphasis is on regularities and typical behaviors.
Even though we all have our subjective reasons why we move and how we do that, from the bird-eye-view movements of inhabitants in a city show big regularities.
In order to understand big picture and behavior of societies, we take computational approach based on data and information.
See the work of Albert-László Barabási who studies networks on different scales:

18. Computation as Information Processing
Info-computational approach takes information as the primary stuff of the universe, and computation is as time-dependent behavior (dynamics) of information.
This results in a Dual-aspect Universe: informational structure with computational dynamics. (Info-Computationalism, Dodig Crnkovic)
Information and computation are closely related – no computation without information, and no information without dynamics (computation).

19. Cognition as computation
Cognition as computation. Information networks at the basis of cognition
100 billions of neurons connected with tiny "wires" in total longer more than two times the earth circumference. This intricate and apparently messy neural circuit that is responsible for our cognition and behavior.
Biophysics of Computation: Information Processing in Single Neurons Christof Koch,

20. Cognition as computation – information processing

21. Cognition as Computation
Information/computation mechanisms are fundamental for evolution of intelligent agents. Their role is to adapt the physical structure and behavior that will increase organisms chances of survival, or otherwise induce some other behavior that might be a preference of an agent.
In this pragmatic framework, meaning in general is use, which is also the case with meaning of information.
hormone-therapy-helps-improve-cognition
2012/04/my-brain-hurts.png

22. Agent-based Models
An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole.
It combines elements of game theory, complex systems, emergence, computational sociology, multi agent systems, and evolutionary programming.
Monte Carlo Methods are used to introduce randomness.
The basic of ABMs the study of complexity and emergence.
Composite Agents (5.06)

23. Agent based modeling with applications to social computing
Agent based modeling with applications to social computing. Computer as a communication device
Even though computers were invented in order to automatize calculations [Hilbert program (1920); Turing Machine (1936)], after a while the importance of the computer as a communication device was recognized, with its important consequent shared knowledge and community-building (Licklider and Taylor 1968).
Licklider, J.C.R. and Taylor R. W. (1968) The computer as a communication device. Science and Technology (September),

24. Approaches to social computing
There are two different approaches to social computing, (Wang et al. 2007), centered on its two different aspects :
 computing mechanisms and principles and
 human aspects of social computing (critical theory)

25. From information communication to social intelligence
Social computing with the focus on social is a phenomenon which enables extended social cognition, while the Social computing with the focus on computing is about computational modelling and it is a new paradigm of computing.

26. Simulation
The main tools in this field are simulation techniques used in order to facilitate the study of society and to support decision-making policies, helping to analyze how changing policies affect social, political, and cultural behavior (Epstein, 2007).
Epstein, J. M. (2007). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University.

27. Emergence of social computing
Social computing is radically changing the character of human relationships worldwide (Riedl, 2011). Instead of maximum 150 connections prior to ICT, (Dunbar, 1998), social computing easily leads to networks of several hundred of contacts.
Dunbar R. (1998) Grooming, Gossip, and the Evolution of Language, Harvard Univ. Press
It remains to understand what type of society will emerge from such massive “long-range” distributed interactions instead of traditional fewer and deeper short-range ones.
Riedl J. (2011) "The Promise and Peril of Social Computing," Computer, vol.44, no.1, pp.93-95

28. Towards social intelligence
In this process, information overload on individuals is steadily increasing, and social computing technologies are moving beyond simple social information communication toward social intelligence, (Zhang et al. 2011) (Lim et al. 2008) (Wang et al. 2007), which brings an additional level of complexity.
Of special interest is the agent-based social simulation (ABSS) as a generative computational approach to social simulation defined by the interactions of autonomous agents whose actions determine the evolution of the system, as applied in artificial life, artificial societies, computational sociology, dynamic network analysis, models of markets, swarming (including swarm robotics).

29. From information communication to social intelligence
As Gilbert (2005) points out, novelty of agent based models (ABMs) “offers the possibility of creating ‘artificial’ societies in which individuals and collective actors such as organizations could be directly represented and the effect of their interactions observed.

30. The emergence of social institutions from individual interaction
This provided for the first time the possibility of using experimental methods with social phenomena, or at least with their computer representations; of directly studying the emergence of social institutions from individual interaction.”
Gilbert N: (2005) Agent-based social simulation: dealing with complexity,

31. Agent-based models
An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi agent systems, and evolutionary programming.
ABMs are very useful computational instruments but they should not be taken as “reality” even though simulations with their realistic graphical representations suggest their being “real”. Process of modeling and simulation is complex and many simplifications and assumptions must be made which always must be justified for each application.

32. Agent-based models
ABMs in general are used to model complex, dynamical adaptive systems. The interesting aspect in ABMs is the micro-macro link (agent-society). Multi-Agent Systems (MAS) models may be used for any number (in general heterogeneous) entities spatially separated by the environment which can be modeled explicitly.
Interactions are in general asynchronous which adds to the realism of simulation.
Social computing represents a new computing paradigm which is one sort of the natural computing, often inspired by biological systems (e.g. swarms).

33. Socio-technological networks as agent-based model
More on agent-based models
Agent Based Model: Information Flows on Networks #1
Pandemic influenza computer model
Composite Agents (5.06)
Delegation & distribution
Modelling dynamical processes in complex socio-technical systems

34. Social computing: social cognition, social networks, social intelligence and multiagent systems
The cross-disciplinary field of Social computing has two main aspects:
Social and
Computational
One focus is on social side of social software or social web applications such as blogs, wikis, social bookmarking, instant messaging, and social networking sites. Social computing often uses crowdsourcing method.

35. Crowdsourcing
Crowdsourcing is, according to the Merriam-Webster Dictionary, the practice of obtaining needed services, ideas, or content by obtaining contributions from a large group of people, and especially from an online community, rather than from traditional employees or suppliers.
Tools such as prediction markets, social tagging, reputation and trust systems as well as recommender systems are based on crowdsourcing.

36. Computational modelling of social behavior
Another focus of social computing is on computational modeling of social behavior, among others through Multi-agent systems (MAS) and Social Networks (SN).
There are several usages of Multi-agent systems: to design distributed and/or hybrid systems; to develop philosophical theory; to understand concrete social facts, or to answer concrete social issues via modelling and simulation.

37. Multi-agent systems for modelling of social behavior
Multi-agent systems are used for modelling, among other things, cognitive or reactive agents who interact in dynamic environments where they possibly depend on each other to achieve their goals.
The emphasis is nowadays on constructing complex computational systems composed by agents which are regulated by various types of norms, and behave like human social systems.

38. Social Networks
Social networks (SN) are social structures made of nodes (which are, generally, individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, ideas, financial exchange, friends, kinship, dislike, conflict, trade, web links, disease transmission, etc.

39. Social Networks
Social networks analysis plays an important role in studying the way specific problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
Social networks analysis has addressed also the dynamics issue, called dynamic networks analysis. This is an emergent research field that brings together traditional social network analysis, link analysis and multi-agent systems.

40. INFORMATION AND COMPUTATION World Scientific Publishing Co
INFORMATION AND COMPUTATION World Scientific Publishing Co. Series in Information Studies, 2011 Gordana Dodig-Crnkovic and Mark Burgin
Brier Søren - Cybersemiotics and the question of knowledge Burgin Mark - Information Dynamics in a Categorical Setting Chaitin Greg - Leibniz, Complexity & Incompleteness Collier John - Information, Causation and Computation Cooper Barry - From Descartes to Turing: The computational Content of Supervenience Dodig Crnkovic Gordana and Mueller Vincent - A Dialogue Concerning Two Possible World Systems Hofkirchner Wolfgang - Does Computing Embrace Self-Organisation? Kreinovich Vladik & Araiza Roberto - Analysis of Information and Computation in Physics Explains Cognitive Paradigms: from Full Cognition to Laplace Determinism to Statistical Determinism to Modern Approach

41. INFORMATION AND COMPUTATION World Scientific Publishing Co
INFORMATION AND COMPUTATION World Scientific Publishing Co. Series in Information Studies, 2011 Gordana Dodig-Crnkovic and Mark Burgin
MacLennan Bruce J. - Bodies — Both Informed and Transformed Menant Christophe - Computation on Information, Meaning and Representations. An Evolutionary Approach Mestdagh C.N.J. de Vey & Hoepman J.H. - Inconsistent information as a natural phenomenon Minsky Marvin - Interior Grounding, Reflection, and Self-Consciousness Riofrio Walter - Insights into the biological computing Roglic Darko- Super-recursive features of natural evolvability processes and the models for computational evolution Shagrir Oron - A Sketch of a Modeling View of Computing Sloman Aaron- What's information, for an organism or intelligent machine? How can a machine or organism mean? Zenil Hector & Delahaye Jean-Paul - On the algorithmic nature of the world

42. A Computable Universe

43. Computating Nature http://dx.doi.org/10.1007/978-3-642-37225-4
Computation, Information, Cognition Editor(s): Gordana Dodig Crnkovic and Susan Stuart, Cambridge Scholars Publishing, 2007
Information and Computation Editor(s): Gordana Dodig Crnkovic and Mark Burgin, World Scientific, 2011
Computing Nature Editor(s): Gordana Dodig Crnkovic and Raffaela Giovagnoli, Springer, 2013

44. Based on the following articles
Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011, 2(3), ; doi: /info Special issue: Selected Papers from FIS 2010 Beijing Conference, See also:
Dodig-Crnkovic, G.; Rotolo, A.; Sartor, G.; Simon, J. and Smith C. (Editors) Social Computing, Social Cognition. Social Network and Multiagent Systems. Social Turn - SNAMAS 2012 AISB/IACAP World Congress Birmingham, UK, 2-6 July 2012http://events.cs.bham.ac.uk/turing12/proceedings/11.pdf , 2012.
Dodig-Crnkovic G., Large-Scale Use of Robots and Meeting Risks with Learning Socio-Technical Organization, IEEE ARSO 2012, Workshop on Advanced Robotics and its Social Inpacts May 2012 at Techniche Universität München, Germany