search path linking theory, model, and
implementation, and suggested possible theories and techniques to develop
sociality-based agents. These incorporate expertise from both ABM and MAS
and require integration of both areas in
order to succeed. We welcome the discussion of these issues toward a novel
area of research on social agents, which
take sociability as the basis for agent deliberation and enable interaction.
1. Chai, S. Choosing an Identity: A General Model
of Preference and Belief Formation. University of
Michigan Press, 2001.
2. Conte, R., Gilbert, N., and Sichman, J. MAS and social
simulation: A suitable commitment. In J. Sichman,
R. Conte, and N. Gilbert, Eds, Multi-Agent Systems
and Agent-Based Simulation, volume 1534 of
Lecture Notes in Computer Science,. Springer Berlin
Heidelberg, 1998, 1–9
3. Davidsson, P. Agent based social simulation: A
computer science view. Journal of Artificial Societies
and Social Simulation 5, 1 (2002).
4. Dias, J., S. Mascarenhas, and A. Paiva. Fatima
modular: Towards an agent architecture with a
generic appraisal framework. In Proceedings of the
International Workshop on Standards for Emotion
5. Dignum, F., Dignum, V., and Jonker, C. M. Towards
agents for policy making. In MABS IX, Springer, 2009,
6. Dignum, F. et al. A conceptual architecture for social
deliberation in multi-agent organizations. Multiagent
and Grid Systems 11, 3 (2015), 147–166.
7. Dignum, F., Prada, R., and Hofstede, G.J. From autistic
to social agents. In AAMAS 2014, May 2014.
8. Dignum, V. Mind as a service: Building socially
intelligent agents. In V. Dignum, P. Noriega, M. Sensoy,
and J. Sichman, Eds, COIN XI: Revised Selected Papers,
Springer International Publishing, 2016, 19–133.
9. Epstein, J.M. and Axtell, R. Growing Artificial Societies:
Social Science from the Bottom Up. The Brookings
Institution, Washington, D. C., 1996.
10. Fiedrich, F. and Burghardt, P. Agent-based systems
for disaster management. Commun. ACM 50, 3 (Mar.
11. Ghorbani, A. Enhancing abm into an inevitable tool
for policy analysis. Policy and Complex Systems 1, 1
12. Gigerenzer, G. Moral satisficing: Rethinking moral
behavior as bounded rationality. Topics in Cognitive
Science 2, 3 (2010), 528–554.
13. Kaminka, G. Curing robot autism: A challenge. In
Proceedings of the AAMAS 2013, May 2013), 801–804.
14. Lindenberg, S. Social rationality versus rational
egoism. In Handbook of Sociological Theory, Springer,
15. Reckwitz, A. Toward a theory of social practices.
European Journal of Social Theory, 5, 2 (2002), 243–263.
16. Rogers, A. et al. The effects of proxy bidding and
minimum bid increments within ebay auctions. ACM
Trans. Web 1, 2 (Aug. 2007).
17. Shove, E., Pantzar, M., and Watson, M. The Dynamics of
Social Practice. Sage, 2012.
18. Silverman, B. et al. Rich socio-cognitive agents for
immersive training environments: The case of nonkin
village. Journal of Autonomous Agents and Multi-Agent Systems 24, 2 (Mar. 2012); 312–343.
19. Vercouter, L. et al. An experience on reputation models
interoperability based on a functional ontology.
In Proceedings of IJ-CAI’07, Morgan Kaufmann
Publishers Inc., San Francisco, CA, 2007, 617–622.
20. Wooldridge, M. Reasoning about Rational Agents. MI T
21. Wooldridge, M. An Introduction to Multiagent
Systems. Wiley, New York, 2009.
Virginia Dignum ( email@example.com) is an associate
professor with the Faculty of Technology, Policy and
Management, Delft University of Technology, Delft,
Copyright held by author.
to the optimization of own wealth, but
often motivated by altruism, fairness,
justice, or by an attempt to prevent regret at a later stage.
˲ Understand when there is no need
to further maximize utility beyond some
reasonably achievable threshold.
˲Understand how identity, culture, and values influence action, and
use this knowledge to decide about
reputation and trust about who and
how to interact.
The first step toward sociality-based
agents is a thorough understanding of
these principles, and open discussion
across disciplines on the grounds and
requirements for sociality from different perspectives. This discussion will
be fundamental to the development of
formal models and agent architectures
that make sociality-based behavior possible and verifiable.
Moreover, it is necessary to identify and formalize which mechanisms,
other than imitation, can describe
how agents can adapt to pressures
in the environment to behave in a socially acceptable, resource-sustainable
fashion. Resulting models support the
understanding or predicting human
behavior, including rich models of
emotions, identities, culture, values,
norms, and many other socio-cognitive characteristics. Such models of
social reality are also needed to study
the complex influences on behavior
of different socio-cognitive characteristics and their relationships. The integration of psychological models of
motivation and cognition, sociological
theories of value and identity formation, and philosophical theories of coherence and higher-order rationality,
together with different formal methods, quickly yields intractable models.
However, it is important to identify
what is the model being developed for.
In fact, richer models are not always
the most appropriate ones.
Once these characteristics are well
understood, then simplified mod-
els can be developed to suit different
needs. That is, implementing sociality-
based agents will require other tech-
niques than those currently used in
either MAS or ABM, 8 including the use
of simpler, context-specific decision
rules, mimicking how people them-
selves are able to deal with complex
decision making, for example, using
social practices as a kind of shortcuts
for deliberation. 15, 17 Where it concerns
utility, satisficing can be more suitable
approach than maximizing. 12 This also
allows us to integrate agents of varied
richness levels, for example, using
rich cognitive models to zoom-in the
behavior of salient agents in a simula-
tion, whereas other agents just follow
simple rules. This approach can coun-
ter the obvious criticism that sociality-
based agents will become too complex
for use in computational simulations.
Sociality-based agents are also fundamental to the new generations of intelligent devices, and interactive characters in smart environments. These
artifacts not only must build (partial)
social models about the humans they
interact with, but also need to take social roles in a mixed human/digital reality. An interesting challenge would be to
use the same technologies in real time
mixed human/artificial interactions,
and criticisms could also be on the feasibility to use these architectures (or
in real time or near real time.
The intent of this Viewpoint has been
to appeal for a collaborative research
effort toward fundamental formal theories and models that increase our understanding of the principles behind
human deliberation (such as the ones
listed discussed here), before deciding
on which modeling techniques we need
to implement them. Even though, several approaches to model social aspects
in agent behavior are available, there is
not sufficient consensus on which characteristics are needed for what, nor on
how to specify and integrate them. We
have identified an initial set of characteristics for sociability, proposed a re-
agents are also
fundamental to the
new generations of