More Like this

1. Visual mate preference evolution during butterfly speciation is linked to neural processing genes

   https://doi.org/10.1038/s41467-020-18609-z  

   Rossi, Matteo; Hausmann, Alexander E.; Thurman, Timothy J.; Montgomery, Stephen H.; Papa, Riccardo; Jiggins, Chris D.; McMillan, W. Owen; Merrill, Richard M. (December 2020, Nature Communications)

   null (Ed.)

   Abstract Many animal species remain separate not because their individuals fail to produce viable hybrids but because they “choose” not to mate. However, we still know very little of the genetic mechanisms underlying changes in these mate preference behaviours. Heliconius butterflies display bright warning patterns, which they also use to recognize conspecifics. Here, we couple QTL for divergence in visual preference behaviours with population genomic and gene expression analyses of neural tissue (central brain, optic lobes and ommatidia) across development in two sympatric Heliconius species. Within a region containing 200 genes, we identify five genes that are strongly associated with divergent visual preferences. Three of these have previously been implicated in key components of neural signalling (specifically an ionotropic glutamate receptor and two regucalcins ), and overall our candidates suggest shifts in behaviour involve changes in visual integration or processing. This would allow preference evolution without altering perception of the wider environment. 

   more » « less
2. Stakeholder Preference Extraction From Scenarios

   https://doi.org/10.1109/TSE.2023.3333265  

   Shen, Yuchen; Breaux, Travis (January 2024, IEEE Transactions on Software Engineering)

   Companies use personalization to tailor user experiences. Personalization appears in search engines and online stores, which include salutations and statistically learned correlations over search-, browsing- and purchase-histories. However, users have a wider variety of substantive, domain-specific preferences that affect their choices when they use directory services, and these have largely been overlooked or ignored. The contributions of this paper include: (1) a grounded theory describing how stakeholder preferences are expressed in text scenarios; (2) an app feature survey to assess whether elicited preferences represent missing requirements in existing systems; (3) an evaluation of three classifiers to label preference words in scenarios; and (4) a linker to build preference phrases by linking labeled preference words to each other based on word position. In this study, the authors analyzed 217 elicited directory service scenarios across 12 domain categories to yield a total of 7,661 stakeholder preferences labels. The app survey yielded 43 stakeholder preferences that were missed on average 49.7% by 15 directory service websites studied. The BERT-based transformer showed the best average overall 81.1% precision, 84.4% recall and 82.6% F1-score when tested on unseen domains. Finally, the preference linker correctly links preference phrases with 90.1% accuracy. Given these results, we believe directory service developers can use this approach to automatically identify user preferences to improve service designs. 

   more » « less
3. Formalizing the fundamental Faustian bargain: Inefficacious decision-makers sacrifice their freedom of choice to coercive leaders for economic security

   https://doi.org/10.1371/journal.pone.0275265  

   DeCaro, Daniel A.; DeCaro, Marci S.; Hotaling, Jared M.; Appel, Rachel (September 2022, PLOS ONE)

   Fernandes, Thiago P. (Ed.)

   Individuals typically prefer the freedom to make their own decisions. Yet, people often trade their own decision control (procedural utility) to gain economic security (outcome utility). Decision science has not reconciled these observations. We examined how decision-makers’ efficacy and security perceptions influence when, why, and how individuals exchange procedural and outcome utility. Undergraduate adults ( N = 77; M age = 19.45 years; 73% female; 62% Caucasian, 13% African American) were recruited from the psychology participant pool at a midwestern U.S. metropolitan university. Participants made financial decisions in easy and hard versions of a paid card task resembling a standard gambling task, with a learning component. During half the trials, they made decisions with a No-Choice Manager who controlled their decisions, versus a Choice Manager who granted decision control. The hard task was designed to be too difficult for most participants, undermining their efficacy and security, and ensuring financial losses. The No-Choice Manager was designed to perform moderately well, ensuring financial gains. Participants felt greater outcome satisfaction (utility) for financial gains earned via Choice, but not losses. Participants (85%) preferred the Choice manager in the easy task but preferred the No-Choice Manager (56%) in the hard task. This change in preference for choice corresponded with self-efficacy and was mediated by perceived security. We used Decision Field Theory to develop potential cognitive models of these decisions. Preferences were best described by a model that assumed decision-makers initially prefer Choice, but update their preference based on loss-dependent attentional focus. When they earned losses (hard task), decision-makers focused more on economic payoffs (financial security), causing them to deemphasize procedural utility. Losses competed for attention, pulling attention toward economic survivability and away from the inherent value of choice. Decision-makers are more likely to sacrifice freedom of choice to leaders they perceive as efficacious to alleviate perceived threats to economic security. 

   more » « less
4. Indecision Modeling

   McElfresh, Duncan C; Chan, Lok; Doyle, Kenzie; Sinnott-Armstrong, Walter; Conitzer, Vincent; Schaich Borg, Jana; Dickerson, John P (April 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   AI systems are often used to make or contribute to important decisions in a growing range of applications, including criminal justice, hiring, and medicine. Since these decisions impact human lives, it is important that the AI systems act in ways which align with human values. Techniques for preference modeling and social choice help researchers learn and aggregate peoples' preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. These techniques often assume that people are willing to express strict preferences over alternatives; which is not true in practice. People are often indecisive, and especially so when their decision has moral implications. The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior---and that there are several different reasons people are indecisive. This complicates the task of both learning and aggregating preferences, since most of the relevant literature makes restrictive assumptions on the meaning of indecision. We begin to close this gap by formalizing several mathematical indecision models based on theories from philosophy, psychology, and economics; these models can be used to describe (indecisive) agent decisions, both when they are allowed to express indecision and when they are not. We test these models using data collected from an online survey where participants choose how to (hypothetically) allocate organs to patients waiting for a transplant. 

   more » « less
5. Social Mechanism Design: Making Maximally Acceptable Decisions

   Abramowitz, Ben; Mattei, Nicholas (July 2023, 9th International Workshop on Computational Social Choice)

   Sometimes agents care not only about the outcomes of collective decisions but also about how decisions are made. Both the outcome and the procedure affect whether agents see a decision as legitimate or acceptable. We focus on incorporating agents’ preferences over decision-making processes into the process itself. Taking whole decisions, including decision rules and outcomes, to be the object of agent preferences rather than only decision outcomes, we (1) identify natural, plausible preference structures and key properties, (2) develop general mechanisms for aggregating these preferences to maximize the acceptability of decisions, and (3) analyze the performance of our acceptance-maximizing mechanisms. We apply our general approach to the setting of dichotomous choice, and compare the worst-case rates of acceptance achievable among populations of agents of different types. We include the special case of rule selection, or amendment, and show that amendment procedures proposed by Abramowitz et al. [2] achieve universal acceptance with certain agent types. 

   more » « less