Sintelnet workshop report:
Computational Social Science and Social
Computer Science: Two Sides of the
Same Coin
Guildford, 23 - 24 June 2014
Virginia Dignum1, Frank Dignum2
1 Technology Policy and Management, Delft University of Technology, The Netherlands
Email: m.v.dignum@tudelft.nl
2 Information and Computing Sciences, Utrecht University, The Netherlands
Email: f.p.m.dignum@uu.nl
The workshop “Computational Social Science and Social Computer Science: Two Sides of the Same
Coin” (http://www.ias.surrey.ac.uk/workshops/computational/) took place in Guildford, Surrey on 23
and 24 June 2014. It was organised by the Institute of Advanced Studies of the University of Surrey in
collaboration with the CRESS, Department of Sociology, University of Surrey. This workshop is a
follow up on the Social.PATH organised last year, and strongly contributes to the aims of Sintelnet, in
facilitating interactions and future research on the principles, theories and artefacts for social
coordination. In this report, we describe the overall contribution of the workshop presentations and
provide our own input to the goals of the working group on Social Coordination (WG5).
1. Workshop overview
The two invited contributions of the workshop came from the two different sides of the coin. One
came from social computer science while the other came from computational social science. Thus
giving different perspectives on social coordination. The other contributions to the workshop were
also well spread over the different disciplines. Most presentations described work in progress and
generated lively discussions.
The first keynote presentation was by Prof. J. Doyne Farmer, from Oxford University, on “The
Challenge of Agent-based Modeling”. In this presentation, Farmer discussed reasons why the full
impact of the computer revolution still remains to be felt in social science, and economics in
particular. He argued that this hurdle is surmountable, and that agent-based modeling, combined
with behavioral experiments and other methods, provides the natural vehicle for doing so. In order
to achieve this a variety of intermediate steps must be accomplished, such as the gathering of much
richer micro data, and the solution of several fundamental problems in calibrating and validating
agent based models.
The second keynote presentation was by Prof. Andreas Flache, University of Groningen, The
Netherlands, on “Computational models of the complexity of social integration”. In this presentation,
Flache presented several case studies from his research group, where simulation has provided
valuable insights into the complexity of social systems.
The workshop included 9 paper presentations and 2 group discussion sessions. The second discussion
was specifically geared towards how to sustain this community in the future. The workshops are seen
as very useful contributions to research in the area and sparked already some new collaborations
and initiatives. So, we discussed how to keep the momentum after the end of the Sintelnet funding.
The participants, 21 in total, originated from the UK, the Netherlands, Sweden and New Zealand.
2. Our contribution to the workshop.
2.1 Paper presentation
We presented our current work on “Situational Deliberation Getting to Social Intelligence”, a
collaboration with Rui Prada from INESC Lisbon, and Catholijn Jonker from the Delft University of
Technology. This work discusses the idea of social artificial intelligence.
Even though there are enormous advances in Artificial Intelligence, Natural Language Processing, and
Vision and Planning, the vision of pioneer AI researchers of truly intelligent systems is still far from
reality. Artificial systems must be endowed with forms of social intelligence engrained in the core of
the systems reasoning, such as have been developed by humans[2]. Social reality is not given, but
socially constructed [3],[1].
Current systems still have very limited understanding of their context, and of their social role. They
are not able to reason about their identity and goals in a social context, and therefore cannot be
expected to function outside the situations they’ve been designed for.
The sociability of current robots and virtual characters is engineered into their system, in a
situational and context dependent way. Social signals are not appraised as such but implicitly built
into their functionality. Therefore, their behaviour is not conceived as social outside that particular
context, and they are not able to adapt to significant changes. This implies that reuse in different
social contexts or cultures requires a complete re-engineering of the system. A next step forward in
AI, is the ability to perceive, reason about and exhibit social intelligent behaviour. This will require a
framework containing explicit social principles that can be described, represented and manipulated
in a symbolic way.
Thus we argue that deliberative, social, and physical principles must be considered first class
components of a computational theory of social intelligence (cf. Figure 1).
Being socially intelligent requires a keen understanding of the principles of social reality, and the
ability to link social interpretations with individual goals into plans and vice versa. The double-bind
represents the interrelationship between social and physical contexts, where the social context
defines the possible social interpretations of the physical reality and limits the set of admissible
actions; and the physical context determines and constrains the possible social contexts. For
example, a raised hand can mean many things: in a class room: a question, in an auction hall: a bid,
on the street: a greeting or a threat.
It is clear that splitting the context in a social and physical one adds quite some complexity. If an
agent plans for a social goal (such as gaining acceptance in a group of soccer fans) it needs to plan
physical actions to reach such a goal. Thus any plan has both social as well as physical consequences.
The agent has to deliberate about all these elements in order to decide upon the best cause of
action. It might be clear that agents need some structures in order to limit and facilitate this process.
In this paper we describe the first steps towards agents which can be called socially intelligent.
2.2 Plans for further initiatives
As result from the discussion groups, and at the suggestion of Virginia Dignum, the organisers of the
workshop have proposed an idea to a FET Proactive topic, entitled “Identifying foundational Hilbert
problems for Social Simulation”. This was the answer from the workshop participants to the public
consultation launched by FET was to identify promising and potentially game-changing directions for
future research in any technological domain. Results of this consultation will be announced after
summer 2014.
Other ideas for further initiatives include an extended workshop at the Lorentz Center in the
Netherlands or Dagstuhl in Germany, and to explore the possibility of proposing a new COST action.
References
1. Berger, P. L. and Luckmann, T. 1966 . The Social Construction of Reality: A Treatise in the
Sociology of Knowledge, New York: Penguin Books
2. C. Breazeal, Social interactions in HRI: the robot view, Systems, Man, and Cybernetics, Part C:
Applications and Reviews 34(2), IEEE pp.181-186, 2004
3. John R. Searle, The Construction of Social Reality, Penguin 1996.