Computing has profoundly changed modern society by changing how people communicate, work, and spend leisure time. Social computing focuses on both the social influence of computers and new types of computation performed by large groups of agents exchanging information in networks. This lecture emphasizes the technological aspects of social computing and its relationship to general models of computing as information processing. Keywords include actors and agent networks, social computing, and info-computationalism.

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

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.
p. 3

4. 4
http://neuralethes-en.blogspot.se/2012/04/human-connectome-project.html
Human Connectome Project
Human brain is biological information
processor - network of neurons processing information

5. 5
http://www.google.com/insidesearch/features/search/knowledge.html
Google Knowledge Graph
Human groups are information processing
networks – knowledge generators

6. 6
http://physicsworld.com/blog/2009/03/the_atlas_of_science.html
Atlas of Science
Sciences are created through
scientists knowledge networks

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

8. 8
Learningandknowledge
http://www.alexeikurakin.org/
Hebbs learning theory:
"cells that fire toghether, wire togher”
Achildisbornwithnervoussystemandabrain
asanetworkofneuronswhichprovide
ccapacitytointeractandlearn.

9. 9
World as information for an agent
From: http://www.alexeikurakin.org

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

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

12. 12
We are part of a
“COGNITIVE
REVOLUTION”
And it is important
to understand how
processes of
information
exchange and
knowledge
generation function.
Information – Knowledge Networks
http://2prowriting.files.wordpress.com/2012/11/trends-in-cognitive-sciences-december-201

13. 13
Knowledge generated by individuals
is shared in groups and society
Bilden från: http://www.alexeikurakin.org

14. 14

15. Networks of networks of information
and knowledge – show complexity
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).
p. 15
In a complex system, what we see is dependent on where we are and what sort of
interaction is used to study the system.
http://www.morphwize.com/company/index.php?option=com_k2&view=itemlist&task=tag&tag=complex+system+solution

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.
● p. 16

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:
http://www.barabasilab.com/pubs-talks.php

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. Information
networks at the basis of cognition
Biophysics of Computation: Information Processing in Single Neurons
Christof Koch, 1999. http://www.klab.caltech.edu/~koch/biophysics-book/
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.
http://www.istc.cnr.it/group/locen

20. p. 20http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full
http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/
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.
http://www.worldhealth.net/news/
hormone-therapy-helps-improve-cognition
http://www.ritholtz.com/blog/wp-content/uploads/
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.
http://www.youtube.com/watch?v=2C2h-vfdYxQ&feature=related Composite
Agents (5.06)
http://en.wikipedia.org/wiki/Agent-based_model
p. 22

23. 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), 20-41.
Agent based modeling with
applications
to social computing.
Computer as a communication device
p. 23

24. 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)
Approaches to social computing
p. 24

25. 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.
From information communication to
social intelligence
p. 25

26. 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.
Simulation
p. 26

27. 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
Emergence of social computing
p. 27
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. 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.
Towards social intelligence
p. 28
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. 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.
From information communication to
social intelligence
p. 29

30. 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,
http://www.complexityscience.org/NoE/ABSS-dealing%20with
%20complexity-1–1.pdf
The emergence of social institutions
from individual interaction
p. 30

31. 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.
Agent-based models
p. 31
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. 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.
Agent-based models
p. 32
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
http://www.nature.com/nphys/journal/v8/n1/full/nphys2160.html
Modelling dynamical processes in complex socio-technical systems
Delegation & distribution
More on agent-based models
http://www.youtube.com/watch?v=pgUT4F8mskQ
Agent Based Model: Information Flows on Networks #1
http://www.youtube.com/watch?v=E_-9hFzmxkw Pandemic
influenza computer model
http://www.youtube.com/watch?v=2C2h-
vfdYxQ&feature=related Composite Agents (5.06)

34. 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.
Social computing: social
cognition, social networks, social
intelligence
and multiagent systems
34

35. ● 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.
Crowdsourcing
35

36. ● 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.
Computational modelling of
social behavior
36

37. ● 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.
Multi-agent systems for
modelling of social behavior
37

38. ● 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.
Social Networks
38

39. ● 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.
Social Networks
39

40. 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
p. 40
INFORMATION AND COMPUTATION
World Scientific Publishing Co. Series in Information Studies, 2011
Gordana Dodig-Crnkovic and Mark Burgin

41. 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
p. 41
INFORMATION AND COMPUTATION
World Scientific Publishing Co. Series in Information Studies, 2011
Gordana Dodig-Crnkovic and Mark Burgin

42. p. 42
A Computable Universe

43. Computation, Information, Cognition
Editor(s): Gordana Dodig Crnkovic and Susan
Stuart, Cambridge Scholars Publishing, 2007
Computating Nature
p. 43
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
http://dx.doi.org/10.1007/978-3-642-37225-4

44. Based on the following articles
● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011,
2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010
Beijing Conference, 2011.
http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing
http://www.mdpi.com/2078-2489/2/3/460/ See also:
http://livingbooksaboutlife.org/books/Energy_Connections
● 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 2012. 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 21-23 May 2012 at Techniche Universität München, Germany
p. 44