Autonomous systems, like drones and self-driving cars, are becoming part of our daily lives. Multiple people interact with them, each with their own expectations regarding system behaviour. To adapt system behaviour to human preferences, we propose and explore a game-theoretic approach. In our architecture, autonomous systems use sensor data to build game-theoretic models of their interaction with humans. In these models, we represent human preferences with types and a probability distribution over them. Game-theoretic analysis then outputs a strategy, that determines how the system should act to maximise utility, given its beliefs over human types. We showcase our approach in a search-and-rescue (SAR) scenario, with a robot in charge of locating victims. According to social psychology, depending on their identity some people are keen to help others, while some prioritise their personal safety. These social identities define what a person favours, so we can map them directly to game-theoretic types. We show that our approach enables a SAR robot to take advantage of human collaboration, outperforming non-adaptive configurations in average number of successful evacuations. CCS CONCEPTS• Computer systems organization → Robotics; • Human-centered computing → Collaborative interaction.

While auction theory views bids and valuations as continuous variables, real-world auctions are necessarily discrete. In this paper, we use a combination of analytical and computational methods to investigate whether incorporating discreteness substantially changes the predictions of auction theory, focusing on the case of uniformly distributed valuations so that our results bear on the majority of auction experiments. In some cases, we find that introducing discreteness changes little. For example, the first-price auction with two bidders and an even number of values has a symmetric equilibrium that closely resembles its continuous counterpart and converges to its continuous counterpart as the discretisation goes to zero. In others, however, we uncover discontinuity results. For instance, introducing an arbitrarily small amount of discreteness into the all-pay auction makes its symmetric, pure-strategy equilibrium disappear; and appears (based on computational experiments) to rob the game of pure-strategy equilibria altogether. These results raise questions about the continuity approximations on which auction theory is based and prompt a re-evaluation of the experimental literature. * For comments and helpful suggestions, we would especially like to thank

Developers continuously invent new practices, usually grounded in hard-won experience, not theory. Game theory studies cooperation and conflict; its use will speed the development of effective processes. A survey of game theory in software engineering finds highly idealised models that are rarely based on process data. This is because software processes are hard to analyse using traditional game theory since they generate huge game models. We are the first to show how to use game abstractions, developed in artificial intelligence, to produce tractable game-theoretic models of software practices. We present Game-Theoretic Process Improvement (GTPI), built on top of empirical game-theoretic analysis. Some teams fall into the habit of preferring "quick-anddirty" code to slow-to-write, careful code, incurring technical debt. We showcase GTPI's ability to diagnose and improve such a development process. Using GTPI, we discover a lightweight intervention that incentivises developers to write careful code: add a single code reviewer who needs to catch only 25% of kludges. This 25% accuracy is key; it means that a reviewer does not need to examine each commit in depth, making this process intervention cost-effective.

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Priority inflation occurs when a Quality-Assurance (QA) engineer or a project manager requesting a feature inflates the priority of their task so that developers deliver the fix or the new functionality more quickly. We survey developers and show that priority inflation occurs and misallocates developer time. We are the first to apply empirical game-theoretic analysis (EGTA) to a software engineering problem, specifically priority inflation. First, we extract prioritisation strategies from 42,620 issues from Apache's JIRA, then use TASKASSESSOR, our EGTA-based modelling approach, to confirm conventional wisdom and show that the common process of a QA engineer assigning priority labels is susceptible to priority inflation. We then show that the common mitigation strategy of having a bug triage team assigning priorities does not resolve priority inflation and slows development. We then use mechanism design to devise assessor-throttling, a new, lightweight prioritization process, immune to priority inflation. We show that assessor-throttling resolves 97% of high priority tasks, 69% better than simply relying on those filing tasks to assign priorities. Finally, we present TheFed, a browser extension for Chrome that supports assessor-throttling.