Economic reasoning and artificial intelligence
TL;DR: This work asks how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.
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Abstract: The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.
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Figures
Figure 3: Each entry gives the utility to (row player, column player). (a) Prisoner’s dilemma. The dominant strategy equilibrium is (Defect, Defect). (b) Mediated Prisoner’s dilemma. The dominant strategy equilibrium is (Mediator, Mediator). 
Figure 4: Two generations of sponsored search mechanisms. Early designs were first price (FP) and advertisers (ADV) used AIs (AI-POS) to maintain a position on the list of search results at the lowest possible price. Second price (SP) auction mechanisms were introduced, designed to replace the combination of FP and AI-POS. Advertisers adopted new AIs (AI-GOAL) to achieve higher-level goals such as maximize profit or maximize the number of clicks. The second price auction was extended to include proxy agents (SP+Proxy), designed to replace the combination of SP and AI-GOAL. 
Figure 2: Researchers produced steady exponential progress on solving games of imperfect information from 1995 to the present. Up to 2007 (left), game size was generally reported in terms of nodes in the game tree. Based on methods introduced around that time, it became more meaningful (right) to report size in terms of the number of information sets (each many nodes), which represent distinct situations as perceived from the perspective of a player. The circled data points correspond to the same milestone; combining the two graphs thus demonstrates the continual exponential improvement. 
Figure 5: In a reputation system for a multi-agent AI, each agent chooses an action, and the combined effect of these actions generates rewards (i.e., utility). Based on the actions taken and rewards received, agent i can submit a report, xi to the reputation system. The reputation system aggregates this feedback, for example providing a ranked list to reflect the estimated trustworthiness of agents. Each agent observes this ranked list, and this information may influence future actions.
Citations
Evaluating Polymer Representations via Quantifying Structure-Property Relationships.
TL;DR: This work investigates different types of polymer representations, including Morgan fingerprint, molecular embedding, and molecular graph, based on the benchmark database from a subset of the well-known web-based polymer databases, PolyInfo.
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An Overview of Applications of Proper Scoring Rules
TL;DR: It is suggested that the wisdom-of-crowds phenomenon might be a driving force behind the recent popularity of proper scoring rules.
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Investigating the applications of artificial intelligence in cyber security
TL;DR: This study promotes the development of theory about AI in cyber security, helps researchers establish research directions, and provides a reference that enterprises and governments can use to plan AI applications in the cyber security industry.
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A multi-agent system for sharing distributed manufacturing resources
TL;DR: The computational experiments show that the EA-MAS, with the integration of shared resource information, is able to provide shared scheduling scheme for distributed manufacturing with a good performance.
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