Peter Duggins

Masters Student in Systems Design Engineering


My fundamental objective is to maximize the wellbeing of intelligences that experience subjectively positive and negative states of consciousness. To realize this goal, I simulate and analyze artificial societies populated with cognitively, emotionally, and socially plausible agents. To develop these agents, I plan to (a) utilize the Neural Engineering Framework to specify how biological brains represent and process information, (b) construct human-like agents by incorporating emotional and social modules into SPAUN, and (c) generate unique personalities for these agents by exposing them to unique developmental regimes. With unlimited access to information in the simulation, I can (d) define quantitative measures of an agent's wellbeing based on its observed brain state. To maximize this quantity across an (e) artificial society composed of agents situated in a virtual environment, I will (f) design controlled experiments that investigate the personal and societal conditions which promote the greatest wellbeing, and finally (g) apply these results back to our reality for the same purpose.

In my masters thesis, I developed methods for incorporating biologically-detailed neuron models (such as those used in the Human Brain Project) into the NEF, then simulated the effects of ADHD and its pharmacological treatments on a model of working memory. It is my hope that these techniques can be used to simulate and study a wide range of biophysical mechanisms in the brain that were previously inaccessible with simple neuron models (i.e. LIF), as well as reduce our reliance on animal experiments and human trials when designing treatments for mental disorders. I also view this is as a necessary step in understanding the neuromodulatory control that emotional systems exert over other cognitive processes, including those governing social interaction. In my doctoral work, I hope to engineer agents capable of emotionally-biased cognition and communication through a combination of neural engineering and reinforcement learning, with the goal of reproducing small-N experiments in social psychology.