Eliminating Group Agency

Aggregating individuals’ consistent attitudes might produce inconsistent collective attitudes. Some groups therefore need the capacity to form attitudes that are irreducible to those of their members. Such groups, group-agent realists argue, are agents in control of their own attitude formation. In this paper, however, I show how group-agent realism overlooks the important fact that groups consist of strategically interacting agents. Only by eliminating group agency from our social explanations can we see how individuals vote strategically to gain control of their groups and produce collective attitudes we cannot make sense of if we treat groups as agents.

Monotonicity in the averaging process

We investigate an averaging process that describes how interacting agents approach consensus through binary interactions. In each elementary step, two agents are selected at random and they reach compromise by adopting their opinion average. We show that the fraction of agents with a monotonically decreasing opinion decays as $e^{-\alpha t}$, and that the exponent $\alpha=\tfrac{1}{2}-\tfrac{1+\ln \ln 2}{4\ln 2}$ is selected as the extremum from a continuous spectrum of possible values. The opinion distribution of monotonic agents is asymmetric, and it becomes self-similar at large times. Furthermore, the tails of the opinion distribution are algebraic, and they are characterized by two distinct and nontrivial exponents. We also explore statistical properties of agents with an opinion strictly above average.

Delay-Induced Periodic Behaviour in Competitive Populations

We study a model of binary decisions in a fully connected network of interacting agents. Individual decisions are determined by social influence, coming from direct interactions with neighbours, and a group level pressure that accounts for social environment. In a competitive environment, the interplay of these two aspects results in the presence of a persistent disordered phase where no majority is formed. We show how the introduction of a delay mechanism in the agent’s detection of the global average choice may drastically change this scenario, giving rise to a coordinated self sustained periodic behaviour.

MODELING OF ADAPTIVE BEHAVIOR PROPERTIES OF PARTICIPANTS OF SOCIO-ECONOMIC PROCESSES

The article deals with the issues of modeling socio-economic systems at the regional level. A promising direction in this area is the use of an agent-based approach based on taking into account the features and a certain autonomy of the behavior of interacting agents. Moreover, one of their key properties is the ability to adapt to changing conditions of functioning. Proceeding from this, in order to solve the problems of increasing the efficiency of developing strategic decisions, it is necessary to develop tools to support management decisions, which will be based on the use of an adaptive simulation model of the region. To do this, the article provides a brief overview of the types of agents depending on the methods of processing and using the information they perceive, an algorithm for the functioning of the agent within the framework of the simulation model is presented. It is shown that the information received by the agent can be of different nature. Separate characteristics of any objects, phenomena form a database. The collection of such information forms a precedent. A set of precedents form a rule base. The analysis showed that the adaptability of the agent's behavior is becoming an essential condition for the implementation of his goals. It should be noted that the agent acts in conditions of incomplete information, and his actions are largely described by the theory of bounded rationality. The structure of perceived information is shown and an approach to reflecting the adaptive properties of an agent through the learning subsystem is proposed. Its elements and implementation options are highlighted. From the point of view of reflecting the adaptive properties of the agent, obtaining new information leads to the replenishment of the database and the base of use cases. At the same time, within the framework of analytical activities, new information is compared with the expectations formed on the basis of previously available data and reflected in the agent's rule base. If the information received is contrary to the existing rules, then this becomes the starting point for their revision. Thus, the adaptation of the agent within the framework of the developed simulation model of the region is based on the adjustment of the rule base.

Co-dependency of exchanged behaviors is a cue for agency attribution in 10-month-olds

Goal-directed social interactions (whether instrumental or communicative) involve co-dependent, partially predictable actions of interacting agents as social goals cannot be achieved by continuously exchanging the same, perfectly predictable, or completely random behaviors. We investigated whether 10-month-olds are sensitive to the co-dependence and degree of predictability in an interactive context where unfamiliar entities exchanged either perfectly predictable (identical), partially predictable (co-dependent), or non-predictable (random) signal sequences. We found that when—following the interactive exchanges—one of the entities turned in the direction of one of two lateral target objects, infants looked more at the indicated referent, but only in the partially predictable signals condition. This shows that infants attributed agency to the orienting entity and interpreted its turning action as a referential object-directed action. The present findings suggest that the co-dependency and partial predictability of exchanged behaviors can serve as an abstract structural cue to attribute intentional agency and recognize goal-directed social interactions.

Temporal Behavioral Parameters of On-Going Gaze Encounters in a Virtual Environment

To navigate the social world, humans heavily rely on gaze for non-verbal communication as it conveys information in a highly dynamic and complex, yet concise manner: For instance, humans utilize gaze effortlessly to direct and infer the attention of a possible interaction partner. Many traditional paradigms in social gaze research though rely on static ways of assessing gaze interaction, e.g., by using images or prerecorded videos as stimulus material. Emerging gaze contingent paradigms, in which algorithmically controlled virtual characters can respond flexibly to the gaze behavior of humans, provide high ecological validity. Ideally, these are based on models of human behavior which allow for precise, parameterized characterization of behavior, and should include variable interactive settings and different communicative states of the interacting agents. The present study provides a complete definition and empirical description of a behavioral parameter space of human gaze behavior in extended gaze encounters. To this end, we (i) modeled a shared 2D virtual environment on a computer screen in which a human could interact via gaze with an agent and simultaneously presented objects to create instances of joint attention and (ii) determined quantitatively the free model parameters (temporal and probabilistic) of behavior within this environment to provide a first complete, detailed description of the behavioral parameter space governing joint attention. This knowledge is essential to enable the modeling of interacting agents with a high degree of ecological validity, be it for cognitive studies or applications in human-robot interaction.

Foundations for Knowledge and Belief Management

Three foundational principles are introduced: intelligent systems such as those that would pass the Turing test should display multi-agent or interactional intelligence; multi-agent systems should be based on conceptual structures common to all interacting agents, machine and human; and multi-agent systems should have an underlying interactional logic such as dialogue logic. In particular, a multi-agent rather than an orthodox analysis of the key concepts of knowledge and belief is discussed. The contrast that matters is the difference between the different questions and answers about the support for claims to know and claims to believe. A simple multi-agent system based on dialogue theory which provides for such a difference is set out.

A Dynamical Generative Model of Social Interactions

The ability to make accurate social inferences makes humans able to navigate and act in their social environment effortlessly. Converging evidence shows that motion is one of the most informative cues in shaping the perception of social interactions. However, the scarcity of parameterized generative models for the generation of highly-controlled stimuli has slowed down both the identification of the most critical motion features and the understanding of the computational mechanisms underlying their extraction and processing from rich visual inputs. In this work, we introduce a novel generative model for the automatic generation of an arbitrarily large number of videos of socially interacting agents for comprehensive studies of social perception. The proposed framework, validated with three psychophysical experiments, allows generating as many as 15 distinct interaction classes. The model builds on classical dynamical system models of biological navigation and is able to generate visual stimuli that are parametrically controlled and representative of a heterogeneous set of social interaction classes. The proposed method represents thus an important tool for experiments aimed at unveiling the computational mechanisms mediating the perception of social interactions. The ability to generate highly-controlled stimuli makes the model valuable not only to conduct behavioral and neuroimaging studies, but also to develop and validate neural models of social inference, and machine vision systems for the automatic recognition of social interactions. In fact, contrasting human and model responses to a heterogeneous set of highly-controlled stimuli can help to identify critical computational steps in the processing of social interaction stimuli.

Network Structured Kinetic Models of Social Interactions

The aim of this paper is to study the derivation of appropriate meso- and macroscopic models for interactions as appearing in social processes. There are two main characteristics the models take into account, namely a network structure of interactions, which we treat by an appropriate mesoscopic description, and a different role of interacting agents. The latter differs from interactions treated in classical statistical mechanics in the sense that the agents do not have symmetric roles, but there is rather an active and a passive agent. We will demonstrate how a certain form of kinetic equations can be obtained to describe such interactions at a mesoscopic level and moreover obtain macroscopic models from monokinetics solutions of those. The derivation naturally leads to systems of nonlocal reaction-diffusion equations (or in a suitable limit local versions thereof), which can explain spatial phase separation phenomena found to emerge from the microscopic interactions. We will highlight the approach in three examples, namely the evolution and coarsening of dialects in human language, the construction of social norms, and the spread of an epidemic.

Sosipatra as a Child and Student

This chapter describes what the early life of Sosipatra might have been like. It reviews the handful of details Eunapius gives about her early years and unique education by itinerant Chaldean daemons and contextualizes these by considering how other girls of Sosipatra’s class and time might have been raised and educated. To understand her education and life, it helps to know three principles about the ancient worldview: events are shaped by external invisible forces; events and actions are the products of multiple interacting agents; and all things, human and nonhuman, visible and invisible, seek to be brought into accord. Education in this Platonic lineage ideally meant assimilation to the cosmos in general and to divinity in particular.