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1. An Experiment on the Effects of Using Color to Visualize Requirements Analysis Tasks

   https://doi.org/10.1109/RE57278.2023.00023  

   Baatartogtokh, Yesugen; Foster, Irene; Grubb, Alicia M. (September 2023, 2023 IEEE 31st International Requirements Engineering Conference (RE))

   Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demon-strated promise in their analytical capabilities; yet, stakeholders have expressed concerns about the readability of the models and resulting analysis, which builds upon Tropos. Tropos is based on formal semantics enabling automated analysis; however, this creates a problem of interpreting evidence pairs. The aim of our broader research project is to improve the process of model comprehension and decision making by improving how analysts interpret and make decisions. We extend and evaluate a prior approach, called EVO, which uses color to visualize evidence pairs. In this scientific evaluation paper, we explore the effectiveness and usability of EVO. We conduct an experiment (n = 32) to measure any effect of using colors to represent evidence pairs. We find that with minimal training, untrained modelers were able to use the color visualization for decision making. The visualization significantly improves the speed of model comprehension and users found it helpful. 

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2. A parameter recovery assessment of a wide class of evidence accumulation models of decision-making.

   https://doi.org/10.31234/osf.io/3ca6y_v1  

   Murrow, Matthew; Holmes, William (May 2024, OSF)

   Computational modeling has become indispensable in investigating the dynamics of decision making processes. A prominent category of models in this domain are Evidence Accumulation Models (EAMs), which model both the decisions people make and the time they take. Many variations have been proposed which modify the drift rate, diffusion rate, and decision thresholds, encoding increasingly complex dynamics into the EAM framework. However, adding model features complicates parameter recovery, making model interpretation more difficult. In this work, we perform a parameter recovery study to a variety of common binary choice EAMs, identify the specific challenges for each, and explore how to improve their parameter recoverability. Though previous studies have addressed this question, they have been piecemeal in nature, with different groups applying different computational methods to study different models. We aim to unify this body of literature using the best currently available computational methods. Further, we present the first, to our knowledge, Bayesian analysis of diffusion conflict models. Our purpose here is to be thorough, not exhaustive or comprehensive. With this in mind, this article catalogues a number of results, some previously shown and some new. Further, it illustrates different approaches to model analysis. This article is intended to be a resource for researchers interested in utilizing EAMs for studying decision-making processes, providing insights into the challenges associated these models, how to analyze them in light of those challenges, and examples of how to address those challenges. 

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3. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

   https://doi.org/10.1145/3579601  

   Zhang, Angie; Walker, Olympia; Nguyen, Kaci; Dai, Jiajun; Chen, Anqing; Lee, Min Kyung (April 2023, Proceedings of the ACM on Human-Computer Interaction)

   Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders---decision makers (faculty) and decision subjects (students)---use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making. 

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4. BIM Driven Retrofitting Design Evaluation of Building Facades

   https://doi.org/10.1145/3565970.3567688  

   Sermarini, John; Michlowitz, Robert A.; LaViola, Joseph J.; Walters, Lori C.; Azevedo, Roger; Kider Jr., Joseph T. (December 2022, SUI '22: Proceedings of the 2022 ACM Symposium on Spatial User Interaction)

   Building facades are components that shape a structure’s daylighting, energy use, and view factors. This paper presents an approach that enables designers to understand the impact that different facade designs will have over time and space in the built environment through a BIM-enabled augmented reality system. The system permits the examination of a range of facade retrofit scenarios and visualizes the daylighting simulations and aesthetics of a structure while retaining function and comfort. A focus of our study was to measure how participants make decisions within the multiobjective decision space designers often face when buildings undergo retrofitting. This process often requires designers to search for a set of alternatives that represent the optimal solution. We analyze the decision-making process of forty-four subjects to determine how they explore design choices. Our results indicate the feasibility of using BIM-enabled AR to improve how designers make informed decisions. 

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5. Bridging adaptive management and reinforcement learning for more robust decisions

   https://doi.org/10.1098/rstb.2022.0195  

   Chapman, Melissa; Xu, Lily; Lapeyrolerie, Marcus; Boettiger, Carl (July 2023, Philosophical Transactions of the Royal Society B: Biological Sciences)

   From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’. 

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