Pablo Lucas

Emotions in Social Interactions: Unfolding Emotional Experience

This chapter takes into account the role of emotions in social interactions, both face-to-face and video-mediated. Emotions are conceptualised as ongoing processes rooted in dynamic social contexts, which can shape both implicit and explicit emotional responses. Emotion interactions are therefore considered as continuously developing, thanks to the relationship between interactants and between them and the surrounding environment. Theories of emotions as non-static phenomena are illustrated before presenting a review of literature on regulating processes in emotional interactions. Finally, based on the theoretical framework described in this chapter, comparisons between an emotionally competent human and an emotionally competent artificial agent are drawn.

Human Societies: Understanding Observed Social Phenomena

The chapter begins by briefly describing two contrasting simulations: the iconic system dynamics model publicised under the Limits to Growth book and a detailed model of first millennium Native American societies in the southwest of the United States. These are used to bring out the issues of abstraction, replicability, model comprehensibility, understanding vs. prediction and the extent to which simulations go beyond what is observed. All of these issues are rooted in some fundamental difficulties in the project of simulating observed societies that are then briefly discussed. Both issues and difficulties result in three “dimensions” in which simulation approaches differ. The core of the chapter is a look at 15 different possible simulation goals, both abstract and concrete, giving some examples of each and discussing them. The different inputs and results from such simulations are briefly discussed as to their importance for simulating human societies.

An Adaptation of the Ethnographic Decision Tree Modeling Methodology for Developing Evidence-Driven Agent-Based Models

This paper introduces the integration of the Ethnographic Decision Tree Modelling methodology into an evidence-driven lifecycle for developing agent-based social simulations. The manuscript also highlights the development advantages of using an Ethnographic Decision Tree Model to promote accountable validation and detailed justification of how agent-based models are built. The result from this methodology is a hierarchical, tree-like structure that represents the branching and possible outcomes of the decision-making process, which can then be implemented in an agent-based model. The original methodology grounds the representation of decision-making solely on ethnographic data, yet the discussed adaptation hereby furthers that by allowing the use of survey data. As a result, the final model is a composite based on a richly descriptive dataset containing observations and reported behaviour of individuals engaged in the same activity and context. This in turn is demonstrated to serve as a useful guide for the implementation of behaviour in an social simulations and also serve as a baseline for testing.

A note on the modelling and interpretation of a public goods game experiment

ABSTRACT This paper presents an alternative interpretation of an experimental public goods game dataset, particularly on the understanding of the observed antisocial behaviour phenomenon between subjects of a public goods experiment in different cities around the world. The anonymous nature of contributions and punishments in this experiment are taken into account to interpret results. This is done by analysing dynamic behaviour in terms of mean contributions across societies and their association with antisocial punishment. By taking into account the heterogeneity between the cities in which the public goods experiment has been performed, this analysis shows a contrasting interpretation. Instead of one trend across cities, two opposite trends are seen across different cities. In addition, we find that the presence of these trends to have an impact on the role of antisocial and pro-social behaviour in public goods games. When accounting for these trends, the antisocial and pro-social behaviour is found to have a significant role in Western societies.

Integrating Collective Decision-Making Models and Agent-Based Simulation

Collective Decision-Making Models (henceforth CDMM) are mathematically deterministic formulations (i.e. without probabilistic inputs or outputs) aimed at explaining the behaviour of individuals in dynamic negotiations given any number of issues, in which the participants attempt to influence the outcome of a final and binding decision. Albeit different CDMM have produced acceptable predictions to actual final collective outcomes, both the data collection process and the interpretation of CDMM results require attention to the rather strict underlying assumptions in each of these models. Our contribution is thus twofold: (I) replication for systematic testing of the Challenge and Exchange CDMM assumptions, along with their requirements consisting of the Compromise, Mean and Median models, using an agent-based framework; and (II) insights gained from these tests regarding the dynamics of CDMM runs and their combinations using input from three datasets.