• Conte: Intelligent Social Learning
• Dautenhahn: Reverse Engineering of Societies - A Biological Perspective
• Doran: The Archaeology of Artificial Societies
• Edmonds: The Inconstructability of Artificial Intelligence by Design - the necessary social development of an agent that can pass the Turing Test
• Fioretti: Recognition of investment opportunities and generation of investment cycles
• Fliedner: About the Problem of Complexity and Emergence. The View of a Social Geographer
• Heitsch, Hinck & Martens: A New Look into Garbage Cans - Petri Nets and Organisational Choice
• Henrickson: Having a Sense of Ourselves: Communications Technology and Personal Identity
• Marshall & Parastatidis:  Modelling Agent Systems Using the Hotel Analogy: Sanitised for your Protection?
• Nehaniv: The Making of Meaning in Societies: Semiotic & Information-Theoretic Background to the Evolution of Communication
• Price & Boutilier: Imitation and Reinforcement Learning in Agents with Heterogeneous Actions
• Schillo, Allen, Fischer & Klein: Socially Competant Business Agents With Attitude: using Habitus-Field Theory to Design Agents with Social Competence
• Staller & Petta: Gounding Social Norms on Emotions
• Wright: The Society of Mind Requires an Economy of Mind

One of the cognitive processes responsible for social propagation is social learning, broadly meant as the process by means of which agents' acquisition of new information is caused or favoured by their being exposed to one another in a common environment. Social learning results from one or other of a number of social phenomena, the most important of which are social facilitation and imitation. In this paper, a general notion of social learning will be defined and the main processes which are responsible for it, namely social facilitation and imitation, will be analysed in terms of the social mental processes they require. A brief analysis of classical definitions of social learning is carried on, showing that a systematic and consistent treatment of this notion is still missing. A general notion of social learning is then introduced and the two main processes which may lead to it, social facilitation and imitation, will be defined as different steps on a continuum of cognitive complexity. Finally, the utility of the present approach is discussed.  The analysis presented in this paper draws upon a cognitive model of social action (cf. Conte & Castelfranchi, 1995; Conte, 1999). The agent model which will be referred to throughout the paper is a cognitive model, endowed with mental properties for pursuing goals and intentions, and for knowledge-based action. To be noted, a cognitive agent is not to be necessarily meant as a natural system, although many examples examined in the paper are drawn from the real social life of humans. Cognitive agents may also be artificial systems endowed with the capacity for reasoning, planning, and decision-making about both world and mental states. The interesting question concerning artificial systems is, what are the mechanisms which must be implemented at the agent level to enable them to learn from one another? Are the mechanisms allowing agents to learn from their physical environment sufficient for them to learn also from or perhaps through their social environment? If not, which additional properties are needed? And, earlier than this, what does social learning mean, which social phenomena are referred to by this notion?

This paper reviews important concepts from biology, Artificial Life and Artificial Intelligence  and relates them to research into synthesising societies. We distinguish  between different types of animal and human societies and discuss the notion of social intelligence. Consequences of social embeddedness for modelling societies at different levels of social organisation and control are elaborated. We distinguish between simulation models of societies and the synthesis of artificial societies. We explain why the Artificial Life bottom-up approach is the most promising direction for reverse engineering of societies. The correspondence between synthesised societies and natural (human, animal) societies is investigated, presenting a hierarchy of synthesised societies with increasing indistinguishability between synthesised and human societies.

Can archaeologists help software engineers unravel what has been happening in an artificial society of intelligent agents? We discuss the methods that archaeologists regularly use and how they relate to the properties of an artificial society and the problems faced in recovering its history. As part of the discussion, an abstract model of a typical artificial society is presented, the structure of the process of interpreting evidence is analysed, and the particular macro-social phenomenon of socio-cultural collapse is considered.

The Turing Test, as originally specified, centres on the ability to perform a social role. The TT can be seen as a test of an ability to enter into normal human social dynamics. In this light it seems unlikely that such an entity can be wholly designed in an ?off-line? mode, but rather a considerable period of training in situ would be required. The argument that since we can pass the TT and our cognitive processes might be implemented as a TM that, in theory, an TM that could pass the TT could be built is attacked on the grounds that not all TMs are constructable in a planned way. This observation points towards the importance of developmental processes that include random elements (e.g. evolution), but in these cases it becomes problematic to call the result artificial. The conclusion is that we will not be able to be able to implement an intelligence using only a design stance, but rather such intelligence requires considerable social development.  In this light the TT can be read as challenging conceptions of intelligence which are disconnected with a social environment.

Innovations cause entrepreneurs' mental models not to hold, generating optimism when innovations open up new fields of activity and pessimism when investments in fields that used to be safe no longer yield the usual returns.   The state of optimism or pessimism in the minds of entrepreneurs  eventually propagates to the whole economy, triggering up- and downswings  of aggregate investments. This paper makes use of an original methodology to compute entrepreneurs' state of confidence, depending on the extent empirical experiences validate their mental models.   It is primarily concerned with decision- making in situations where unexpected events (e.g. new technologies) appear.

Complexity, self organization, and emergence are difficult problems which have not yet been satisfactorily resolved. Systems and process research allow more far-reaching conclusions. There are 6 complexity levels:

1. solid bodies ("solida") and "movements",
2. self ordering "equilibrium systems" and "movement projects",
3. self regulating "flow equilibrium systems" and "flow processes",
4. self organizing "non-equilibrium systems" and "conversion processes",
5. structurally self creating "hierarchy systems" and "hierarchical processes"(not treated here),
6. a materially self creating "universal system" and "universal process"(not treated here).

In particular, it is necessary to distinguish flow equilibrium systems and non-equilibrium systems. The socalled complexity research did not sufficiently take this into consideration until now. Flow equilibrium systems distribute, and non-equilibrium systems convert information and energy. Non-equilibrium systems are limited by boundaries, consist of components which cooperate differently. Information and energy flows are separated and the elements have their specific tasks. The systems are internally vertically divided into 4 "bonding" levels which are characterized by the "main", the "task", the "control" resp. the "elementary processes". They are hierarchically ordered and must be performed completely, if the system is to fulfil its task of converting energy from one form in another as effectively as possible. This can be illustrated using an industrial company as an example. Further examples of non-equilibrium systems are perhaps biotic populations, organisms, cells, atoms, stars, etc.

The "emergence processes" intervene between the complexity levels and show the way from the simple to the complex. They always follow (geometrically) the same pattern, i.e. in accordance with a certain code:

1. Bundling: The processes of many elements [these are the solida or folded systems (see below, Folding) of the next lowest complexity level] are bundled in order to become components of the new process. In this way, the extent (amount) is fixed.
2. Alignment: These bundled processes are aligned to a comprehensive new process possessing 4 (or a multiple of 4) part processes. In this way, the number of process stages in this new complexity level is increased 4-fold in relation to the previous one.
3. Interlacement: The new process is now interwoven according to the new dimension constituting the complexity level concerned, i.e. the original basic orientation of the process, vertical or horizontal, is reversed by 90° in all its elements.
4. Folding: In the final stage of the complexity process, one half of the process is folded behind the other in such a way that the beginning and the end of the process come into contact with one another. In this way, a limitation and possible control become possible, and the overall process continued.

 

Understanding how organisations make decisions is crucial step towards understanding organisations. Seeing organisations as a place of structure and rationality has not led to satisfying results. The "Garbage Can Model of Organizational Choice" of Cohen, March, and Olsen (1972), fundamental to behaviouristic organisational theory, looks at "organized anarchies" and opens eyes for ambiguous and unpredictable decision situations. Reference Nets, a high-level Petri net formalism, offer formal semantics, graphical representation, means to model concurrency, and immediate executability, and, thus, seem to meet basic requirements to model and present sociological theories. In this paper Petri nets are used to formalise the Garbage Can Model and expose its implicit assumptions. The resulting model serves as a basis for interdisciplinary collaboration. Weaknesses of the original theory are laid open leading to new sociological considerations.

The rapidity of technological advancement staggers the imagination and catches many people off-guard as they try to absorb the impact of learning new technologies, new tools, new ways of knowing.  Reactions toward new technologies can elicit resistance and adoption.  This paper explores the character of resistance and adoption of technology from the theoretical perspectives of instrumentalism and critical theory.  Key to this analysis is the interplay between human senses and technology as it alters notions of personal identity and of social world views.  The implications of identity alteration affect both computational modeling researchers and educators.  In particular, computational modeling researchers who wish to incorporate socially constructed identity into their models learn that personal identity is not fixed in time or space, and that the use of electronic technology plays a role in such changes.  More broadly, for educators the implications are explicitly focused on developing multiple literacies in anticipation of the changing role that human senses play in communications technologies.

This paper looks at how a particular social analogy (that of the hotel) could be used to help the design of the environment provided by an agent support system. It discusses some of the implementation issues and problems that the use of the analogy exposes.

We examine the notions of meaning and information for animals or agents engaged in interaction games. Concepts from cognitive ethology, linguistics, semiotics, and evolution are surveyed. Innateness, individual learning, and social aspects (social learning and cultural transmission) of the evolution of communication are treated. Studies on animals and agents showing degrees of communication are analyzed with an eye to describing what aspects of communication actually are demonstrated, or also in the case of many simulation studies, are built-in to the system at the outset. In particular, predication and constituent structure (subcategorization) have so far never been shown to emerge in robotic or software systems.

We study the problem of accelerating reinforce-ment learning through the observation and im-plicit imitation of expert agents (mentors) acting in the same domain. In this paper, we consider problems that arise when the learner and mentor have heterogeneous actions. We extend an ear-lier implicit imitation model to allowfor feasibil-ity testing (determining whether a specific mentor action can be duplicated) and repair (discovering a "plan" that simulates a mentor's trajectory) and demonstrate empirically that both of these com-ponents allowlearning agents to learn much more readily than standard RL agents and implicit imi-tation agents without these extended capabilities.

We will argue that social competence is an emergent mental phenomenon, and as such, there is no requirement to build
discrete "social" modules into an agent. In fact, we argue that there are definite advantages to be gained from the emergent approach to social competence in complex, open, multi-agent environments. In order to capitalise on these advantages we need to design socially competent agents with the ability to reason on different levels (reactive, deliberative, meta) within complex social situations. By analysing the sociological theory of Pierre Bourdieu, we describe the design of a socially competent agent through the instantiation of a generic layered agent architecture. Our instantiation provides a methodology for specifying heuristics and parameters for different layers of such architectures. Furthermore, Bourdieu's habitus-field theory is hybrid in the sense that it tries to explain the effect of individual behaviour on societal structures and vice versa. This is where the great strength of the theory lies, and where we expect a useful cross-fertilisation of ideas into AI to occur. For as much as space permits, we will illustrate our argument with a scenario from the domain of shipping companies. This scenario is defined by its openness, diversity of agents as well as tasks and time restrictions. Our work leads us to the conclusion that building social agent architectures has definite engineering advantages, underlining the importance of this concept for both MAS and DAI research.

We argue that modelling emotions among agents in artificial societies will further the computational study of social norms. The appraisal theory of emotions is presented as theoretical underpinning of Jon Elster's view that social norms are sustained not only by material sanctions but also by emotions such as shame and contempt. Appraisal theory suggests the following twofold relationship between social norms and emotions: First, social norms play an important role in the generation of emotions; second, emotion regulation depends heavily on the influence of social norms. Based on these insights, we present an emotion-based view on the influential study by Conte and Castelfranchi (1995); without mentioning emotions, they argue that a function of social norms is aggression control. Appraisal theory offers a principled framework for the development of TABASCO, a three-layer agent architecture incorporating social norms. At the macro level, the computational study of social norms can profit by economic and sociobiological theories, which suggest that emotions play an important role in sustaining norms of cooperation and reciprocity. We show how appraisal theory can serve as a link between the macro and micro levels, and summarize the potential benefits from the development of TABASCO.

A society of mind will require an economy of mind, that is multi-agent systems that meet a requirement for the adaptive allocation and reallocation of scarce resources will need to employ a quantitative, universal, and domain-independent representation of value that mirrors the flow of agent products, much as money is used in simple commodity economies. The money-commodity in human economic systems is shown to be an emergent exchange convention that serves both to constrain and allow the formation of commitments by functioning as an ability to buy processing power. Multi-agent systems with both currency flow and minimally economic agents can adaptively allocate and reallocate control relations and scarce resources, in particular labour or processing power. The implications of this design hypothesis for cognitive science and economics are outlined.