RESUMEN
People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing. Although nudges are often designed with psychological theories in mind, they are typically not formalized in computational terms and their effects can be hard to predict. As a result, designing nudges can be difficult and time-consuming. To address this challenge, we propose a computational framework for understanding and predicting the effects of nudges. Our approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces-that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this framework can account for known effects of nudges based on default options, suggested alternatives, and information highlighting. In each case, we validate the model's predictions in an experimental process-tracing paradigm. We then show how the framework can be used to automatically construct optimal nudges, and demonstrate that these nudges improve people's decisions more than intuitive heuristic approaches. Overall, our results show that resource-rational analysis is a promising framework for formally characterizing and constructing nudges. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Asunto(s)
Conducta de Elección , Toma de Decisiones , Humanos , HeurísticaRESUMEN
Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and evaluate mitigation strategies. Here we show under controlled laboratory conditions that transmission through social networks amplifies motivational biases on a simple artificial decision-making task. Participants in a large behavioural experiment showed increased rates of biased decision-making when part of a social network relative to asocial participants in 40 independently evolving populations. Drawing on ideas from Bayesian statistics, we identify a simple adjustment to content-selection algorithms that is predicted to mitigate bias amplification by generating samples of perspectives from within an individual's network that are more representative of the wider population. In two large experiments, this strategy was effective at reducing bias amplification while maintaining the benefits of information sharing. Simulations show that this algorithm can also be effective in more complex networks.
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Algoritmos , Red Social , Humanos , Teorema de Bayes , Sesgo , MotivaciónRESUMEN
Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a vast distributed system. Viewing human cognition through the lens of distributed systems motivates new questions about how humans share computation, when it makes sense to do so, and how we can build institutions to facilitate collaboration.
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Cognición , Metáfora , Animales , HumanosRESUMEN
Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed computation at the group level can pool information and resources, allowing limited individuals to perform effectively on complex tasks.
Asunto(s)
Inteligencia , Conocimiento , Teorema de Bayes , HumanosRESUMEN
Finding sustainable ways to increase the amount of private land protected for biodiversity is challenging for many conservation organizations. In some countries, organizations use revolving-fund programs, whereby land is purchased and then sold to conservation-minded owners under condition they enter into a conservation covenant or easement. The sale proceeds are used to purchase, protect, and sell additional properties, incrementally increasing the amount of protected private land. Because the effectiveness of this approach relies on selecting appropriate properties, we explored factors currently considered by practitioners and how these are integrated into decision making. We conducted exploratory, semistructured interviews with managers from each of the 5 major revolving funds in Australia. Responses indicated although conservation factors are important, financial and social factors are also highly influential. A major determinant was whether the property could be resold within a reasonable period at a price that replenishes the fund. To facilitate resale, often selected properties include the potential for the construction of a dwelling. Practitioners face with clear trade-offs between conservation, financial, amenity, and other factors in selecting properties and 3 main challenges: recovering the costs of acquisition, protection, and resale; reselling the property; and meeting conservation goals. Our findings suggest the complexity of these decisions may constrain revolving-fund effectiveness. Drawing from participant responses, we identified potential strategies to mitigate these risks, such as providing adequate recreational space without jeopardizing ecological assets. We suggest managers could benefit from a shared-learning and adaptive approach to property selection given the commonalities between programs. Understanding how practitioners deal with complex decisions in the implementation of revolving funds helps identify future research to improve the performance of this conservation tool.