Social Cognitive Maps, Swarm Collective Perception
and Distributed Search on Dynamic Landscapes
Vitorino Ramos1, Carlos Fernandes2,3, Agostinho C. Rosa2,4
1 CVRM-IST, Technical Univ. of Lisbon (IST),
Av. Rovisco Pais, 1, 1049-001, Lisbon, PORTUGAL
vitorino.ramos@alfa.ist.utl.pt
2 LaSEEB-ISR-IST, Technical Univ. of Lisbon (IST),
Av. Rovisco Pais, 1, TN 6.21, 1049-001, Lisbon, PORTUGAL
{cfernandes,acrosa}@laseeb.org
3 EST-IPS, Setúbal Polytechnic Institute (IPS),
R. Vale de Chaves - Estefanilha, 2810, Setúbal, PORTUGAL
4 Psycho Biological Dep., Univ. of São Paulo, São Paulo, SP, BRAZIL


Abstract. Swarm Intelligence (SI) is the property of a system whereby the collective behav-
iors of (unsophisticated) entities interacting locally with their environment cause coherent
functional global patterns to emerge. SI provides a basis with which it is possible to explore
collective (or distributed) problem solving without centralized control or the provision of a
global model. To tackle the formation of a coherent social collective intelligence from individ-
ual behaviors, we discuss several concepts related to self-organization, stigmergy and social
foraging in animals. Then, in a more abstract level we suggest and stress the role played not
only by the environmental media as a driving force for societal learning, as well as by positive
and negative feedbacks produced by the many interactions among agents. Finally, presenting a
simple model based on the above features, we will address the collective adaptation of a social
community to a cultural (environmental, contextual) or media informational dynamical land-
scape, represented here – for the purpose of different experiments – by several three-
dimensional mathematical functions that suddenly change over time. Results indicate that the
collective intelligence is able to cope and quickly adapt to unforeseen situations even when
over the same cooperative foraging period, the community is requested to deal with two differ-
ent and contradictory purposes.
1 Introduction
Swarm Intelligence (SI) is the property of a system whereby the collective behaviors
of (unsophisticated) entities interacting locally with their environment cause coherent
functional global patterns to emerge. SI provides a basis with which it is possible to
explore collective (or distributed) problem solving without centralized control or the
provision of a global model (Stan Franklin, Coordination without Communication,
talk at Memphis Univ., USA, 1996). The well-know bio-inspired computational para-
digms know as ACO (Ant Colony Optimization algorithm [8]) based on trail forma-
tion via pheromone deposition / evaporation, and PSO (Particle Swarm Optimization
[24]) are just two among many successful examples. Yet, and in what specifically
relates to the biomimicry of these and other computational models, much more can be
Comment: Submitted to Brains,
Minds & Media – Journal of New
Media in Neural and Cognitive
Science, NRW, Germany, for his
inaugural issue. March, 2005.

of useful employ, namely the social foraging behavior theories of many species,
which can provide us with consistent hints to algorithmic approaches for the con-
struction of social cognitive maps, self-organization, coherent swarm perception and
intelligent distributed search, with direct applications in a high variety of social sci-
ences and engineering fields. In the present work, we will address the collective adap-
tation of a social community to a cultural (environmental, contextual) or informa-
tional dynamical landscape, represented here – for the purpose of different experi-
ments – by several three-dimensional mathematical functions that change over time.
Flocks of migrating birds and schools of fish are familiar examples of spatial self-
organized patterns formed by living organisms through social foraging. Such aggre-
gation patterns are observed not only in colonies of organisms as simple as single-cell
bacteria, as interesting as social insects like ants and termites as well as in colonies of
multi-cellular vertebrates as complex as birds and fish but also in human societies
[14]. Wasps, bees, ants and termites all make effective use of their environment and
resources by displaying collective “swarm” intelligence. For example, termite colo-
nies build nests with a complexity far beyond the comprehension of the individual
termite, while ant colonies dynamically allocate labor to various vital tasks such as
foraging or defense without any central decision-making ability [8,53]. Slime mould
is another perfect example. These are very simple cellular organisms with limited
motile and sensory capabilities, but in times of food shortage they aggregate to form a
mobile slug capable of transporting the assembled individuals to a new feeding area.
Should food shortage persist, they then form into a fruiting body that disperses their
spores using the wind, thus ensuring the survival of the colony [30,44,53].
New research suggests that microbial life can be even richer: highly social, intricately
networked, and teeming with interactions [47]. Bassler [3] and other researchers have
determined that bacteria communicate using molecules comparable to pheromones.
By tapping into this cell-to-cell network, microbes are able to collectively track
changes in their environment, conspire with their own species, build mutually benefi-
cial alliances with other types of bacteria, gain advantages over competitors, and
communicate with their hosts - the sort of collective strategizing typically ascribed to
bees, ants, and people, not to bacteria. Eshel Ben-Jacob [6] indicate that bacteria have
developed intricate communication capabilities (e.g. quorum-sensing, chemotactic
signalling and plasmid exchange) to cooperatively self-organize into highly structured
colonies with elevated environmental adaptability, proposing that they maintain lin-
guistic communication. Meaning-based communication permits colonial identity,
intentional behavior (e.g. pheromone-based courtship for mating), purposeful altera-
tion of colony structure (e.g. formation of fruiting bodies), decision-making (e.g. to
sporulate) and the recognition and identification of other colonies – features we might
begin to associate with a bacterial social intelligence. Such a social intelligence,
should it exist, would require going beyond communication to encompass unknown
additional intracellular processes to generate inheritable colonial memory and com-
monly shared genomic context. Moreover, Eshel [5,4] argues that colonies of bacteria
are able to communicate and even alter their genetic makeup in response to environ-
mental challenges, asserting that the lowly bacteria colony is capable of computing
better than the best computers of our time, and attributes to them properties of crea-
tivity, intelligence, and even self-awareness.