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1. Deserv: Decentralized Serverless Computing

   Christie, Samuel H.; Chopra, Amit K.; Singh, Munindar P. (January 2021, Proceedings of the 19th IEEE International Conference on Web Services)

   null (Ed.)

   A decentralized application involves multiple autonomous principals, e.g., humans and organizations. Autonomy motivates (1) specifying a decentralized application via a protocol that captures the interactions between the principals, and (2) a programming model that enables each principal to independently (from other principals) construct its own protocol-compliant agent. An agent encodes its principal's decision making and represents it in the application. We contribute Deserv, the first protocol-based programming model for decentralized applications that is suited to the cloud. Specifically, Deserv demonstrates how to leverage function-as-a-service (FaaS), a popular serverless programming model, to implement agents. A notable feature of Deserv is the use declarative protocols to specify interactions. Declarative protocols support implementing stateful agents in a manner that naturally exploits the concurrency and autoscaling benefits offered by serverless computing. 

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2. Agent Based Resilient Transportation Infrastructure With Surrogate Adaptive Networks

   https://doi.org/10.1115/DETC2020-22568  

   Apostol, Andre A.; Turner, Cameron J. (August 2020, c. ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract Connected autonomous intelligent agents (AIA) can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or with other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making can benefit autonomous vehicle technology. This research examines vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication strategies that use gathered data to ensure these agents make appropriate decisions under operational circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection to ultimately find an traffic flow that will minimizes average vehicle transit time to improve intersection efficiency. By considering each light and vehicle as an agent and providing for communication between agents, additional decision-making data can be transmitted. Improving agent based I2I communication and decision making will provide performance benefits to traffic flow capacities. 

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3. Markets for Public Decision-Making

   Garg, Nikhil; Goel, Ashish; Plaut, Benjamin (December 2018, Web and Internet Economics - 14th International Conference)

   A public decision-making problem consists of a set of issues, each with multiple possible alternatives, and a set of competing agents, each with a preferred alternative for each issue. We study adaptations of market economies to this setting, focusing on binary issues. Issues have prices, and each agent is endowed with artificial currency that she can use to purchase probability for her preferred alternatives (we allow randomized outcomes). We first show that when each issue has a single price that is common to all agents, market equilibria can be arbitrarily bad. This negative result motivates a different approach. We present a novel technique called "pairwise issue expansion", which transforms any public decision-making instance into an equivalent Fisher market, the simplest type of private goods market. This is done by expanding each issue into many goods: one for each pair of agents who disagree on that issue. We show that the equilibrium prices in the constructed Fisher market yield a "pairwise pricing equilibrium" in the original public decision-making problem which maximizes Nash welfare. More broadly, pairwise issue expansion uncovers a powerful connection between the public decision-making and private goods settings; this immediately yields several interesting results about public decisions markets, and furthers the hope that we will be able to find a simple iterative voting protocol that leads to near-optimum decisions. 

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4. Algorithmic Decision-Making under Agents with Persistent Improvement

   Xie, Tian; Tan, Xuwei; Zhang, Xueru (August 2024, 7th AAAI Conference on AI, Ethics, and Society)

   This paper studies algorithmic decision-making under human's strategic behavior, where a decision maker uses an algorithm to make decisions about human agents, and the latter with information about the algorithm may exert effort strategically and improve to receive favorable decisions. Unlike prior works that assume agents benefit from their efforts immediately, we consider realistic scenarios where the impacts of these efforts are persistent and agents benefit from efforts by making improvements gradually. We first develop a dynamic model to characterize persistent improvements and based on this construct a Stackelberg game to model the interplay between agents and the decision-maker. We analytically characterize the equilibrium strategies and identify conditions under which agents have incentives to improve. With the dynamics, we then study how the decision-maker can design an optimal policy to incentivize the largest improvements inside the agent population. We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements. With the extended models, we further examine conditions under which agents prefer honest efforts over dishonest behavior and the impacts of forgettable efforts. 

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5. Social Mechanism Design: Making Maximally Acceptable Decisions

   Abramowitz, Ben; Mattei, Nicholas (July 2023, 9th International Workshop on Computational Social Choice)

   Sometimes agents care not only about the outcomes of collective decisions but also about how decisions are made. Both the outcome and the procedure affect whether agents see a decision as legitimate or acceptable. We focus on incorporating agents’ preferences over decision-making processes into the process itself. Taking whole decisions, including decision rules and outcomes, to be the object of agent preferences rather than only decision outcomes, we (1) identify natural, plausible preference structures and key properties, (2) develop general mechanisms for aggregating these preferences to maximize the acceptability of decisions, and (3) analyze the performance of our acceptance-maximizing mechanisms. We apply our general approach to the setting of dichotomous choice, and compare the worst-case rates of acceptance achievable among populations of agents of different types. We include the special case of rule selection, or amendment, and show that amendment procedures proposed by Abramowitz et al. [2] achieve universal acceptance with certain agent types. 

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