Jory Schossau

C. Adami, Jory Schossau, A. Hintze

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations.

A. Hintze, J. Edlund, Randal S. Olson, David B. Knoester, Jory Schossau, L. Albantakis, Ali Tehrani-Saleh, Peter D. Kvam, Leigh Sheneman, Heather Goldsby, Clifford Bohm, C. Adami

Markov Brains are a class of evolvable artificial neural networks (ANN). They differ from conventional ANNs in many aspects, but the key difference is that instead of a layered architecture, with each node performing the same function, Markov Brains are networks built from individual computational components. These computational components interact with each other, receive inputs from sensors, and control motor outputs. The function of the computational components, their connections to each other, as well as connections to sensors and motors are all subject to evolutionary optimization. Here we describe in detail how a Markov Brain works, what techniques can be used to study them, and how they can be evolved.

Joshua S MacCready, Jory Schossau, K. Osteryoung, Daniel C. Ducat

M. Ålund, N. Emery, B. J. Jarrett, Kirsty J Macleod, H. McCreery, N. Mamoozadeh, J. G. Phillips, Jory Schossau, Andrew W. Thompson, Alexa R. Warwick, Kelsey M. Yule, E. Zylstra, E. Gering

Jory Schossau, A. Hintze