Signal-Driven Swarming: Evolution of Coordinated Motion in Groups of Embodied Agents
From biological cells to bee swarms and bird flocks, nature shows countless examples of self-organized groups displaying a collective mind. In such species, individuals interacting together end up producing an emergent behavior that increases their chances of survival and reproduction. We constructed a model first to study swarming behavior based on local interactions [Witkowski & Ikegami 2013, 2016] [Drozd, Witkowski et al. 2015], and secondly to analyze the advantages of such behavior when the agents are playing a spatial n-player Prisoner’s Dilemma [Witkowski et al. 2013]. Our results show the dynamics and stability of emergent collective strategies in nature and society. Recently, we expanded the work into a study of open-ended evolution [Witkowski & Ikegami 2019].
Evolution of Coordination and Communication in Groups of Embodied Agents
In Evolutionary Biology and Game Theory, there is a long history of models aimed at predicting strategies adopted by agents during resource foraging. In Artificial Life, the agent-based modeling approach allowed to simulate the evolution of foraging behaviors in populations of artificial agents embodied in a simulated environment. Part of my PhD dissertation focused on modeling the emergence of communication [Witkowski 2015].
In this project, we showed the influence of honest signaling for evolutionary stable strategies in variable environments, with results on synchronization with resource availability functions [Witkowski et al. 2012], resource-saving strategies [Witkowski & Aubert 2012] and the transmission of migratory behaviors [Witkowski & Nitschke 2013].
Emergence of Autonomy and Agency in Complex Systems
The spontaneous generation of life has long been a central question investigated in the study of the origins of life. We attempt to address this question with two different approaches: information theory and artificial chemistry. We first construct an artificial chemistry model, simulating a system composed of chemical substances, either simulated with interaction rules and with more or less coarse-grained structures or implemented in vitro. We also design a collection of information-theoretical measures aimed to identify autonomous subprocesses in a system, which allows us to divide and conquer the dynamical space. Our early results suggest new ways to quantify the emergence of individuality in early life. [PDF]
Swarm Ethics: Evolution of Cooperation in a Multi-Agent Foraging Model
“It brings out the animal in us” is often heard, when speaking of unaltruistic behavior. Frans de Waal has argued against a ” veneer theory ” of one of humanity’s most valued traits: morality. It has been proposed that morality emerges as a result of a system of evolutionary processes, giving rise to social altruistic instincts. Traditional research has been arguing that fully-fledged cognitive systems were required to give each individual its autonomy. In this paper, we propose that a simple sense of morality can evolve from swarms of agents picking actions such that they are viable to the survival of the whole group. In order to illustrate the emergence of a moral sense within a community of individuals, we use an asynchronous evolutionary model, simulating populations of simulated agents performing a foraging task on a two-dimensional map. We discuss the morality of each emergent behavior within each population, then subsequently analyze several cases of interactions between different evolved foraging strategies, which we argue bring some insight on the concept of morality out of a group, or across species.This proposed approach brings a new perspective on the way morality can be studied in an artificial model, in terms of adaptive behavior, corroborating the argument in which morality can be defined not only in highly cognitive species, but across all levels of complexity in life. [PDF]
Superorganisms: Self-Organizing Particles under Variable Internal vs. External Competition
Game theory models have been demonstrated as indispensable analytical tools to complement our understanding of the emergence of natural phenomena such as cooperation and competition. However, game theory models typically lack consideration for complex phenomena such as evolutionary and environmental change in shaping emergent social phenomena. Agent-Based Models (ABMs) are well established as complementary bottom-up computational tools for studying the impact of specific environmental and evolutionary conditions on emergent social phenomena such as cooperation and competition and for generating data to support game theory model predictions. We empirically test the tug-of-war game theory hypothesis that cooperation in Eusocial insect colonies is driven by the dynamics of within-group cooperation and between-group competition rather than genetic relatedness. That is, increased between-group competition leads to more within-group cooperation and increased group fitness, regardless of average genetic relatedness. We use an ABM implementation of previous tug-of-war game-theoretic models. In this project, we attempt to investigate the influence of competition intra- and inter-group on the evolution of cooperative behavior in different species of simulated agents. [PDF]
Gene-Culture Coevolution: A Model of Language Evolution via Relaxation of Selection
In this project, we used a agent-based model approach to study the evolution of fully- fledged languages, specifically focusing on gene-culture coevolution and the Baldwin effect [McCrohon & Witkowski 2011]. Our results showed interesting transition effects due to local attractors and population size.