Necrotic Behavioral Control of Agent Behavior in the Iterated Prisoner's Dilemma

As the Covid-19 pandemic of 2020 illustrates, controlling the behavior of social agents is a difficult problem. This study examines the potential for an immune-inspired technique called necrosis to steer the behavior of agent populations that are evolving to play the iterated version of the game prisoner's dilemma. A key factor in this is detection of behavioral types. The use of a previously developed technique for fingerprinting the behavior of game playing agents, even complex ones, permits the modelling of control strategies with necrotic behavioral control (NBC). NBC consists of reducing the fitness of agents engaging in an unacceptable behavior. The impact of applying necrosis to a number of agent behaviors is investigated. The strategies always-defect, always-cooperate, and tit-for-two-tats are used as the foci for behavior control by zeroing out the fitness of agents whose behavior is similar to those agents. Our experiments demonstrate that NBC changes the distribution of prisoner's dilemma strategies that arise both when the focal strategy is changed and when the similarity radius used to zero out agent fitness is changed. Filtration focused on the strategy tit-for-two-tats has the largest impact on the evolution of prisoner's dilemma strategies while always cooperate is found to have the least. © 2021 IEEE

We Are Not Pontius Pilate: Acknowledging Ethics and Policy

A new AI system is being developed to optimize vaccination strategies based on the structure and shape of a community's social contact network. The technology is minimally constrained and not hound by preconceived notions or human biases. With this come novel outside the box strategies;however, the system is only capable of optimizing what it is instructed to optimize, and does not consider any ethical or political concerns. With the growing concern for systematic discrimination as a result of artificial intelligence, we acknowledge a number of relevant issues that may arise as a consequence of our new technology and categorize them into three classes. We also introduce four normative ethical approaches that are used as a framework for decision-making. Despite the focus on vaccination strategies, our goal is to improve the discussions surrounding public concern and trust over artificial intelligence and demonstrate that artificial intelligence practitioners are addressing these concerns.

Vaccinating a Population is a Programming Problem

It is important to understand how best to apply a limited number of vaccines to a population such that the spread of a disease, like SARS-CoV-2, is minimized. Although intuition provides a number of mitigation strategies that may be effective, they remain largely untested. A system was developed to test a given disease mitigation strategy. It is designed to work with a graph representing a real social network. A Genetic Programming system was used to discover novel mitigation strategies that are easily interpretable by a public health decision maker. Effective strategies were developed by the GP system. The strategies are easily explainable and intuitive. Novel mitigation strategies were compared to simple baseline strategies with varying success using a number of different metrics. Many of these strategies proved effective in general, however the topology of the graph influences the effectiveness of a strategy. The system has been made publicly available and the authors call on the research community to contribute their own mitigation strategies and measure their efficacy.