Abstract Understanding why animals (including humans) choose one thing over another is one of the key questions underlying the fields of behavioural ecology, behavioural economics and psychology. Most traditional studies of food choice in animals focus on simple, single‐attribute decision tasks. However, animals in the wild are often faced with multi‐attribute choice tasks where options in the choice set vary across multiple dimensions. Multi‐attribute decision‐making is particularly relevant for flower‐visiting insects faced with deciding between flowers that may differ in reward attributes such as sugar concentration, nectar volume and pollen composition as well as non‐rewarding attributes such as colour, symmetry and odour. How do flower‐visiting insects deal with complex multi‐attribute decision tasks?Here we review and synthesise research on the decision strategies used by flower‐visiting insects when making multi‐attribute decisions. In particular, we review how different types of foraging frameworks (classic optimal foraging theory, nutritional ecology, heuristics) conceptualise multi‐attribute choice and we discuss how phenomena such as innate preferences, flower constancy and context dependence influence our understanding of flower choice.We find that multi‐attribute decision‐making is a complex process that can be influenced by innate preferences, flower constancy, the composition of the choice set and economic reward value. We argue that to understand and predict flower choice in flower‐visiting insects, we need to move beyond simplified choice sets towards a view of multi‐attribute choice which integrates the role of non‐rewarding attributes and which includes flower constancy, innate preferences and context dependence. We further caution that behavioural experiments need to consider the possibility of context dependence in the design and interpretation of preference experiments.We conclude with a discussion of outstanding questions for future research. We also present a conceptual framework that incorporates the multiple dimensions of choice behaviour.
more »
« less
Attribute latencies causally shape intertemporal decisions
Abstract Intertemporal choices – decisions that play out over time – pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.
more »
« less
- Award ID(s):
- 2333979
- PAR ID:
- 10516239
- Publisher / Repository:
- Nature Communications
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Viale, R. (Ed.)Alternative-based approaches to decision making generate overall values for each option in a choice set by processing information within options before comparing options to arrive at a decision. By contrast, attribute-based approaches compare attributes (such as monetary cost and time delay to receipt of a reward) across options and use these attribute comparisons to make a decision. Because they compare attributes, they may not use all available information to make a choice, which categorizes many of them as heuristics. Attribute-based models can better predict choice compared to alternative-based models in some situations (e.g., when there are many options in the choice set, when calculating an overall value for an option is too cognitively taxing). Process data comparing alternative-based and attribute-based processing obtained from eye-tracking and mouse-tracking technology support these findings. Data on attribute-based models thus align with the notion of bounded rationality that people make use of heuristics to make good decisions when under time pressure, informational constraints, and computational constraints. Further study of attribute-based models and processing would enhance our understanding of how individuals process information and make decisions.more » « less
-
Network-based analyses have effectively understood customer preferences through interactions between customers and products, particularly for tailored product design. However, research applying this analysis to diverse customers with varied preferences is limited. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers’ two-stage decision-making process: consideration-then-choice. In heterogeneous markets, customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, this method segments customers based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using joint correspondence analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer–product networks and apply the exponential random graph model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how understanding customer preferences at different decision stages can inform and refine design strategies, ensuring products align with diverse market needs.more » « less
-
Abstract Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant.Discrete choice modelsare a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.more » « less
-
Time preferences have been correlated with a range of life outcomes, yet little is known about their early development. We conduct a field experiment to elicit time preferences of over 1200 children ages 3–12, who make several intertemporal decisions. To shed light on how such primitives form, we explore various channels that might affect time preferences, from background characteristics to the causal impact of an early schooling program that we developed and operated. Our results suggest that time preferences evolve substantially during this period, with younger children displaying more impatience than older children. We also find a strong association with race: black children, relative to white or Hispanic children, are more impatient. Finally, assignment to different schooling opportunities is not significantly associated with child time preferences.more » « less
An official website of the United States government

