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1. Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

   https://doi.org/10.3390/info15060351  

   Jeong, Dong Hyun; Jeong, Bong Keun; Ji, Soo Yeon (June 2024, Information)

   In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools. 

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2. Should We Dangle a Carrot? The Effect of Performance-based Incentives in Visualization Experiments

   Sarma, Abhraneel; Kay, Matthew; Long, Sheng; Correll, Michael; Lex, Alexander (January 2027, IEEE transactions on visualization and computer graphics)

   A perennial research question in visualization involves identifying which visual encodings for a particular dataset are most effective for users in performing a specific task. The relative effectiveness of the different encodings are commonly identified through controlled experiments. However, designing an experiment involves making many, often ad hoc, decisions about the experimental setup such as whether to include a training module, whether to provide performance-based incentives to participants, etc. Yet, there is limited guidance on how these decisions should be made, and we do not fully understand the impact of these subjective decisions on empirical results. In this paper, we investigate the impact of one such key design decision: monetary rewards. Specifically, we ask: does providing or not providing participants with performance-based financial incentives affect the results and the conclusions that we draw from visualization studies? We conducted two crowdsourced studies investigating the impact of incentives on (i) a low-level, perceptual task (perception of correlations in scatterplots or parallel coordinate plots), and (ii) a task involving reasoning (decision-making based on a weather forecast represented as intervals or density plots). In each of these studies, we manipulate both the visual representation and the presence of incentives as between-subject conditions. We expected to find no effect of incentives on the perceptual task, but to see an effect for the decision-making task. However, we found no effect on task performance in either study. While these are results of only two studies and should be replicated, they suggest that performance-based financial incentives may not always have the intended effect on participants that we presumed, and calls for a reflection of how incentivized studies should be designed. 

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3. Information Sharing in Design Teams under Competition: Effect on Decision Quality and Search Efficiency

   Javadpour, Nikoo; Mutlu, Kaan; Sha, Zhenghui; Bayrak, Alparslan Emrah (July 2026, Journal of mechanical design)

   For team-based decisions under uncertainty, sharing information plays a crucial role in improving decision quality by reducing uncertainty. This study investigates how information sharing within a team affects individual decisions under uncertainty when they compete with another team. We present the results of an online team-based experiment where 20 participants with engineering backgrounds engaged in a black-box function optimization game, under two conditions: non-information sharing (no information shared between team members) and limited information-sharing (with partial information shared between team members). Each game included eight rounds in which participants sampled input values to maximize an unknown function with a limited budget while competing against another team. We analyze behavioral metrics in decision time, solution quality, effort, and overall payoff. Results show that participants in the information-sharing condition achieved lower solution quality in design-space proximity metrics, but achieved higher average payoffs with fewer attempts, improving efficiency. Decision time also increased significantly in the information-sharing condition, indicating slower but more thoughtful decision-making in information-sharing teams. Qualitative analysis of post-experiment surveys showed that participants adopted more cautious ``follow-the-leader'' strategies when information was shared, often converging on local optima rather than broad exploration. These findings highlight a critical trade-off between performance and efficiency with information sharing in team decision-making where teams can conserve resources and achieve higher average payoffs, but at the cost of solution quality. 

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4. The Effect of a 3-Minute Mindfulness Intervention, and the Mediating Role of Maximization, on Critical Incident Decision-Making

   https://doi.org/10.3389/fpsyg.2021.674694  

   Shortland, Neil D.; McGarry, Presley; Thompson, Lisa; Stevens, Catherine; Alison, Laurence J. (May 2021, Frontiers in Psychology)

   Objective In this study, we extend the impact of mindfulness to the concept of least-worst decision-making. Least-worst decisions involve high-uncertainty and require the individual to choose between a number of potentially negative courses of action. Research is increasingly exploring least-worst decisions, and real-world events (such as the COVID-19 pandemic) show the need for individuals to overcome uncertainty and commit to a least-worst course of action. From sports to business, researchers are increasingly showing that “being mindful” has a range of positive performance-related benefits. We hypothesized that mindfulness would improve least-worst decision-making because it would increase self-reflection and value identification. However, we also hypothesized that trait maximization (the tendency to attempt to choose the “best” course of action) would negatively interact with mindfulness. Methods Three hundred and ninety-eight participants were recruited using Amazon MTurk and exposed to a brief mindfulness intervention or a control intervention (listening to an audiobook). After this intervention, participants completed the Least-Worst Uncertain Choice Inventory for Emergency Responders (LUCIFER). Results As hypothesized, mindfulness increased decision-making speed and approach-tendencies. Conversely, for high-maximizers, increased mindfulness caused a slowing of the decision-making process and led to more avoidant choices. Conclusions This study shows the potential positive and negative consequences of mindfulness for least-worst decision-making, emphasizing the critical importance of individual differences when considering both the effect of mindfulness and interventions aimed at improving decision-making. 

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5. Reframing wildlife disease management problems with decision analysis

   https://doi.org/10.1111/cobi.14284  

   McEachran, Margaret C; Harvey, Johanna A; Mummah, Riley O; Bletz, Molly C; Teitelbaum, Claire S; Rosenblatt, Elias; Rudolph, F Javiera; Arce, Fernando; Yin, Shenglai; Prosser, Diann J; et al (May 2024, Conservation Biology)

   Abstract Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade‐offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision‐making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision‐making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision‐making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white‐nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision‐making, including competing objectives, risks, prediction uncertainty, and limited resources. 

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