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1. Positive Feedback and Self-Reflection: Features to Support Self-efficacy among Underrepresented Job Seekers

   https://doi.org/10.1145/3313831.3376717  

   Dillahunt, Tawanna R.; Hsiao, Joey Chiao-Yin (January 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Technologies play a key role in finding employment in today's job market. However, the majority of those who are unemployed, e.g., individuals who have limited education or who are racial and ethnic minorities, are not well supported by existing digital employment tools. Therefore, we conducted an 8-month randomized field experiment to evaluate two tools-Review-Me and Interview4-designed to address these job seekers' key employment needs. We used the Theory of Planned Behavior to examine the tools' effects on three factors influencing job seekers' job search intention: job search self-efficacy, subjective norms, and job search attitudes. Our interview data suggested that the tools positively affected all factors, but our survey results were mixed. Interview results suggest that these trends were caused by positive feedback and self-reflection. We contribute ways to integrate these two features into future tools for, and techniques to increase study retention among, underrepresented job seekers. 

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2. Photography and Exploration of Tourist Locations Based on Optimal Foraging Theory

   https://doi.org/10.1109/TCSVT.2019.2915103  

   Rawat, Yogesh Singh; Shah, Mubarak; Kankanhalli, Mohan S (May 2019, IEEE Transactions on Circuits and Systems for Video Technology)

   Animals search for food in their environment with a decision strategy which keeps them fit. Optimal Foraging Theory models this foraging behavior to determine the optimal decision strategy followed by animals. This theory has been successfully applied for humans as they search for information and is termed as Information Foraging. When people visit a tourist location, they follow a similar strategy to move from one spot to another and collect information by capturing photographs. This behavior has similarities with the foraging behavior of animals which has been widely studied by researchers. In this work, we propose to employ Optimal Foraging Theory to help tourists explore a location and capture photographs in an optimal way. We determine a decision strategy for tourist which provides a list of interesting spots to visit in a tourist location along with corresponding stay time. Finally, we solve an optimization problem to find a path through these spots which can be followed by tourists. Experimental results on a public dataset demonstrate the effectiveness of the proposed method. 

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3. Toward a Model-Based Experimental Approach to Assessing Collective Systems Design

   https://doi.org/10.1115/DETC2018-85786  

   Valencia-Romero, Ambrosio; Grogan, Paul T. (August 2018, ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   This work presents a conceptual model of collective decision-making processes in engineering systems design to understand the tradeoffs, risks, and dynamics between autonomous but interacting design actors. The proposed approach combines value-driven design, game theory, and simulation experimentation to study how technical and social factors of a design decision-making process facilitate or inhibit collective action. The collective systems design model considers two levels of decision-making: 1) lower-level design value exploration; and 2) upper-level design strategy selection. At the first level, the actors concurrently explore two strategy-specific value spaces with coupled design decision variables. Each collective decision is mapped to an individual scalar measure of preference (design value) that each actor seeks to maximize. At the second level, each of the actor’s design values from the two lower-level design exploration tasks is assigned to one diagonal entry of a normalform game, with off-diagonal elements calculated in function of the “sucker’s” and “temptation-to-defect” payoffs in a classical strategy game scenario. The model helps generate synthetic design problems with specific strategy dynamics between autonomous actors. Results from a preliminary multi-agent simulation study assess the validity of proposed design spaces and generate hypotheses for subsequent studies using human subjects. 

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4. Encouraging Inferable Behavior for Autonomy: Repeated Bimatrix Stackelberg Games with Observations

   https://doi.org/10.23919/ACC60939.2024.10644936  

   Karabag, Mustafa O; Smith, Sophia; Fridovich-Keil, David; Topcu, Ufuk (July 2024, Proceedings of the American Control Conference)

   When interacting with other non-competitive decision-making agents, it is critical for an autonomous agent to have inferable behavior: Their actions must convey their intention and strategy. For example, an autonomous car's strategy must be inferable by the pedestrians interacting with the car. We model the inferability problem using a repeated bimatrix Stackelberg game with observations where a leader and a follower repeatedly interact. During the interactions, the leader uses a fixed, potentially mixed strategy. The follower, on the other hand, does not know the leader's strategy and dynamically reacts based on observations that are the leader's previous actions. In the setting with observations, the leader may suffer from an inferability loss, i.e., the performance compared to the setting where the follower has perfect information of the leader's strategy. We show that the inferability loss is upper-bounded by a function of the number of interactions and the stochasticity level of the leader's strategy, encouraging the use of inferable strategies with lower stochasticity levels. As a converse result, we also provide a game where the required number of interactions is lower bounded by a function of the desired inferability loss. 

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5. A traveler-centric mobility game: Efficiency and stability under rationality and prospect theory

   https://doi.org/10.1371/journal.pone.0285322  

   Chremos, I.V.; Malikopoulos, A.A. (May 2023, PloS one)

   In this paper, we study a routing and travel-mode choice problem for mobility systems with a multimodal transportation network as a “mobility game” with coupled action sets. We formulate an atomic routing game to focus on the travelers’ preferences and study the impact on the efficiency of the travelers’ behavioral decision-making under rationality and prospect theory. To control the innate inefficiencies, we introduce a mobility “pricing mechanism,” in which we model traffic congestion using linear cost functions while also considering the waiting times at different transport hubs. We show that the travelers’ selfish actions lead to a pure-strategy Nash equilibrium. We then perform a Price of Anarchy and Price of Stability analysis to establish that the mobility system’s inefficiencies remain relatively low and the social welfare at a NE remains close to the social optimum as the number of travelers increases. We deviate from the standard game-theoretic analysis of decision-making by extending our mobility game to capture the subjective behavior of travelers using prospect theory. Finally, we provide a detailed discussion of implementing our proposed mobility game. 

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