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1. Behavioral Stable Marriage Problems

   Martin, Andrea; Venable, Kristen Brent; Mattei, Nicholas (December 2021, The Third International Conference on Distributed Artificial Intelligence)

   The stable marriage problem (SMP) is a mathematical abstraction of two-sided matching markets with many practical applications including matching resident doctors to hospitals and students to schools. Several preference models have been considered in the context of SMPs including orders with ties, incomplete orders, and orders with uncertainty, but none have yet captured behavioral aspects of human decision making, e.g., contextual effects of choice. We introduce Behavioral Stable Marriage Problems (BSMPs), bringing together the formalism of matching with cognitive models of decision making to account for multi-attribute, non-deterministic preferences and to study the impact of well known behavioral deviations from rationality on two core notions of SMPs: stability and fairness. We analyze the computational complexity of BSMPs and show that proposal-based approaches are affected by contextual effects. We then propose and evaluate novel ILP and local-search-based methods to efficiently find optimally stable and fair matchings for BSMPs. 

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2. New pathways for women’s empowerment in pastoralist Maasai households, Tanzania

   https://doi.org/10.1016/j.jrurstud.2024.103333  

   Baird, Timothy D; Woodhouse, Emily; McCabe, J Terrence; Barnes, Paul; Terta, Felista; Runda, Naomi (July 2024, Journal of Rural Studies)

   Despite the extensive scholarship on women's empowerment and gender equality in the Global South, few studies have examined how changing livelihoods create new challenges and opportunities for women seeking access to intra-household decision-making. Here we examine pastoralist Maasai women's access to a range of household-level decisions that span more longstanding and more recent aspects of changing social and economic life. Our team conducted a mixed-methods data collection in 10 Maasai communities in northern Tanzania in 2018 and 2022. We (1) interviewed groups of women and men (n = 18) to identify key types of household decisions and the factors affecting women's access to them; and (2) conducted a survey of married women (n = 321) to identify individuals' perceptions of access to intra-household decision-making and other characteristics. We applied an information theoretic approach to model selection of fitted cumulative link mixed effects models. Our findings show that newer sources of human, social, and physical capital for women, including school-based education, land tenure, and community group membership, are associated with access to more contemporary decision types, including income generation, children's schooling, and children's health care. Alternatively, we find fewer pathways to decision-making for more longstanding decision types, including livestock management and children's marriage. Notably, agricultural land has a complex relationship with decision-making wherein basic access to land is associated with lower access to decision-making, but land tenure is associated with greater access. This study shows how marginalized women can leverage changing social and economic contexts to gain greater access to intra-household decision-making. 

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3. Computational mechanisms for context-based behavioral interventions: A large-scale analysis

   https://doi.org/10.1073/pnas.2114914119  

   Zhao, Wenjia Joyce; Coady, Aoife; Bhatia, Sudeep (April 2022, Proceedings of the National Academy of Sciences)

   Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making. 

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4. The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

   Knittel, Marina; Dooley, Samuel; Dickerson, John P. (July 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence)

   While the stable marriage problem and its variants model a vast range of matching markets, they fail to capture complex agent relationships, such as the affiliation of applicants and employers in an interview marketplace. To model this problem, the existing literature on matching with externalities permits agents to provide complete and total rankings over matchings based off of both their own and their affiliates' matches. This complete ordering restriction is unrealistic, and further the model may have an empty core. To address this, we introduce the Dichotomous Affiliate Stable Matching (DASM) Problem, where agents' preferences indicate dichotomous acceptance or rejection of another agent in the marketplace, both for themselves and their affiliates. We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches. Our results are threefold: (1) we use a human study to show that real-world matching rankings follow our assumed valuation function; (2) we prove that there always exists a stable solution by providing an efficient, easily-implementable algorithm that finds such a solution; and (3) we experimentally validate the efficiency of our algorithm versus a linear-programming-based approach. 

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5. Increasing behavioral richness and managing structural uncertainty in social-ecological system agent-based models

   https://doi.org/10.18174/sesmo.18749  

   Magliocca, Nicholas; Pathak, Ruchie; Price, Ashleigh; Tanveer, Hashir; Kumar, Mukesh; Moradkhani, Hamid (February 2024, Socio-Environmental Systems Modelling)

   Responding to the challenges of societal transformation in the face of climate change, efforts to integrate behaviorally rich models of adaptation decision-making into large-scale macroeconomic and Earth system models are growing and agent-based models (ABMs) are an effective tool for doing so. However, behavioral richness in ABMs has been limited to implementations of single decision models for all agents in a simulated population. The main goals of this study were to: 1) implement the ‘building-block processes’ (BBPs) approach for decision model heterogeneity; 2) demonstrate the application of sensitivity and uncertainty analyses to quantify the scope of structural uncertainty produced by alternative decision models under variable price and climate conditions; and 3) apply the Observing System Simulation Experiment (OSSE) approach to validate such a behaviorally rich BBPs model at the level of individual agent decisions. Using an ABM of agricultural producers’ decision-making, we demonstrated that uncertainty in crop and farm management decisions introduced by heterogeneous decision models was equal to and in some instances greater than that due to variable price or precipitation conditions. Unrealistically rapid or stagnant behavioral dynamics were evident in model versions implementing single decision models for all agents. Moreover, interactions among agents with diverse decision models in the same population produced consistently more accurate outcomes and realistic behavioral dynamics. The BBPs framework and accompanying sensitivity and uncertainty analyses demonstrated here offer a path forward for increasing behavioral richness in ABMs, which is key to understanding processes of adaptation central to societal responses to climate change. 

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