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1. Prosociality in Microtransit

   https://doi.org/10.1613/jair.1.16777  

   Sundaresan, Divya; Watson, Akhira; Bardaka, Eleni; Lee, Crystal Chen; Mayhorn, Christopher B; Singh, Munindar P (January 2025, Journal of Artificial Intelligence Research)

   We study (public) microtransit, a type of transportation service wherein a municipality offers point-to-point rides to residents, for a fixed, nominal fare. Microtransit exemplifies practical resource allocation problems that are often over-constrained in that not all ride requests (pickup and dropoff locations at specified times) can be satisfied or satisfied only by violating soft goals such as sustainability, and where economic signals (e.g., surge pricing) are not applicable—they would lead to unethical outcomes by effectively coercing poor people.We posit that instead of taking rider preferences as fixed, shaping them prosocially will lead to improved societal outcomes. Prosociality refers to an attitude or behavior that is intended to benefit others. This paper demonstrates a computational approach to prosociality in the context of a (public) microtransit service for disadvantaged riders. Prosociality appears as a willingness to adjust one’s pickup and dropoff times and locations to accommodate the schedules of others and to enable sharing rides (which increases the number of riders served with the same resources).This paper describes an interdisciplinary study of prosociality in microtransit between a transportation researcher, psychologists, a social scientist, and AI researchers. Our contributions are these: (1) empirical support for the viability of prosociality in microtransit (and constraints on it) through interviews with drivers and focus groups of riders; (2) a prototype mobile app demonstrating how our prosocial intervention can be combined with the transportation backend; (3) a reinforcement learning approach to model a rider and determine the best interventions to persuade that rider toward prosociality; and (4) a cognitive model of rider personas to enable evaluation of alternative interventions. 

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2. Carbon, Climate, and Collapse: Coupling Climate Feedbacks and Resource Dynamics to Predict Societal Collapse

   https://doi.org/10.3390/systems13090727  

   Savitsky, Greta; Burnett, Grace; Beckage, Brian (September 2025, Systems)

   Anthropogenic climate change threatens production of essential natural resources, including food, fiber, and fresh water, and provisioning of ecosystem services such as carbon sequestration, increasing the risk of societal collapse. The Human and Nature Dynamics (HANDY) model simulates the effect of resource overexploitation on societal collapse but lacks representation of feedbacks between climate change and resource regeneration in ecological systems. We extend the HANDY model by integrating models of climate change and ecological function to examine the risk of societal collapse. We conducted a sensitivity analysis of our expanded model by systematically varying key parameters to examine the range of plausible socio-ecological conditions and evaluate model uncertainty. We find that lowered greenhouse gas emissions and resilient ecosystems can delay societal collapse by up to approximately 500 years, but that any scenario with greater than net-zero greenhouse gas emissions ultimately leads to societal collapse driven by climate-induced loss of ecosystem function. Reductions in greenhouse gas emissions are the most effective intervention to delay or prevent societal collapse, followed by the conservation and management of resilient ecological systems to sequester atmospheric carbon. 

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3. Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds

   Kallus, Nathan; Zhou, Angela (January 2019, Advances in neural information processing systems)

   Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to evaluate the fairness of such policies, we show that actually auditing their disparate impacts per standard observational metrics, such as true positive rates, is impossible since ground truths are unknown. Whether our data is experimental or observational, an individual's actual outcome under an intervention different than that received can never be known, only predicted based on features. We prove how we can nonetheless point-identify these quantities under the additional assumption of monotone treatment response, which may be reasonable in many applications. We further provide a sensitivity analysis for this assumption via sharp partial-identification bounds under violations of monotonicity of varying strengths. We show how to use our results to audit personalized interventions using partially-identified ROC and xROC curves and demonstrate this in a case study of a French job training dataset. 

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4. The Role of Pairing Sustainability with Innovation Driven Learning: Observation on the Application of the Engineering-for-One-Planet Framework Guided by the Renaissance Foundry Model

   Wagale, D; Arce-Trigatti, A; Arce, P E; Sanders, J R (March 2024, American Society for Engineering Education Southeastern Annual Meeting)

   This work in progress investigates how the role of an educational intervention that coupled sustainability principles with an innovation-driven learning platform guides students through the development of a protype of innovative technology. Specifically, the intervention includes the purposeful pairing of the Engineering for One Planet (EOP) framework1 with the Renaissance Foundry model (i.e., the Foundry)2 in an undergraduate chemical engineering course that requires student teams to address societal challenges as learning outcomes. We argue that pairing the EOP framework with the Foundry results in an increase in students' sustainability efforts in the design of their prototype of innovative technology that addresses identified societal challenges. A preliminary analysis is presented comparing outcomes from two semesters of the CHE 3550, Transfer Science II (Fluids), course, which is a three-credit hour course with an additional one credit of laboratory work (CHE 3551). Preliminary implications related to holistic engineering education efforts and socially relevant learning will be presented and discussed. 

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5. Adaptive capacity beyond the household: a systematic review of empirical social-ecological research

   https://doi.org/10.1088/1748-9326/ac68fb  

   Vallury, Sechindra; Smith, Ada P.; Chaffin, Brian C.; Nesbitt, Holly K.; Lohani, Sapana; Gulab, Sabrina; Banerjee, Simanti; Floyd, Theresa M.; Metcalf, Alexander L.; Metcalf, Elizabeth C.; et al (May 2022, Environmental Research Letters)

   Abstract The concept of adaptive capacity has received significant attention within social-ecological and environmental change research. Within both the resilience and vulnerability literatures specifically, adaptive capacity has emerged as a fundamental concept for assessing the ability of social-ecological systems to adapt to environmental change. Although methods and indicators used to evaluate adaptive capacity are broad, the focus of existing scholarship has predominately been at the individual- and household- levels. However, the capacities necessary for humans to adapt to global environmental change are often a function of individual and societal characteristics, as well as cumulative and emergent capacities across communities and jurisdictions. In this paper, we apply a systematic literature review and co-citation analysis to investigate empirical research on adaptive capacity that focus on societal levels beyond the household. Our review demonstrates that assessments of adaptive capacity at higher societal levels are increasing in frequency, yet vary widely in approach, framing, and results; analyses focus on adaptive capacity at many different levels (e.g. community, municipality, global region), geographic locations, and cover multiple types of disturbances and their impacts across sectors. We also found that there are considerable challenges with regard to the ‘fit’ between data collected and analytical methods used in adequately capturing the cross-scale and cross-level determinants of adaptive capacity. Current approaches to assessing adaptive capacity at societal levels beyond the household tend to simply aggregate individual- or household-level data, which we argue oversimplifies and ignores the inherent interactions within and across societal levels of decision-making that shape the capacity of humans to adapt to environmental change across multiple scales. In order for future adaptive capacity research to be more practice-oriented and effectively guide policy, there is a need to develop indicators and assessments that are matched with the levels of potential policy applications. 

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