How short- and long-run aspirations impact search and choice in decisions from experience

Pay One or Pay All? The Role of Incentive Schemes in Decision Making Across Adulthood

OBJECTIVES

This research addresses how younger and older adults' decisions and evaluations of gains and losses are affected by the way in which monetary incentives are provided. We compared 2 common incentive schemes in decision making: pay one (only a single decision is incentivized) and pay all (incentives across all decisions are accumulated).

METHOD

Younger adults (18-36 years; n = 147) and older adults (60-89 years; n = 139) participated in either a pay-one or pay-all condition and made binary choices between two-outcome monetary lotteries in gain, loss, and mixed domains. We analyzed participants' decision quality, risk taking, and psychometric test scores. Computational modeling of cumulative prospect theory served to measure sensitivity to outcomes and probabilities, loss aversion, and choice sensitivity.

RESULTS

Decision quality and risk aversion were higher in the gain than mixed or loss domain, but unaffected by age. Loss aversion was higher, and choice sensitivity was lower in older than younger adults. In the pay-one condition, the value functions were more strongly curved, and choice sensitivity was higher than in the pay-all condition.

DISCUSSION

An opportunity of accumulating incentives has similar portfolio effects on younger and older adults' decisions. In general, people appear to decide less cautiously in pay-all than pay-one scenarios. The impact of different incentive schemes should be carefully considered in aging and decision research.

Chasing Unknown Bandits: Uncertainty Guidance in Learning and Decision Making

In repeated decision problems for which it is possible to learn from experience, people should actively seek out uncertain options, rather than avoid ambiguity or uncertainty, in order to learn and improve future decisions. Research on human behavior in a variety of multiarmed-bandit tasks supports this prediction. Multiarmed-bandit tasks involve repeated decisions between options with initially unknown reward distributions and require a careful balance between learning about relatively unknown options (exploration) and obtaining high immediate rewards (exploitation). Resolving this exploration-exploitation dilemma optimally requires considering not only the estimated value of each option, but also the uncertainty in these estimations. Bayesian learning naturally quantifies uncertainty and hence provides a principled framework to study how humans resolve this dilemma. On the basis of computational modeling and behavioral results in bandit tasks, I argue that human learning, attention, and exploration are guided by uncertainty. These results support Bayesian theories of cognition and underpin the fundamental role of subjective uncertainty in both learning and decision making.

Effect of uncertainty visualizations on myopic loss aversion and the equity premium puzzle in retirement investment decisions

For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term losses, leading them to invest less in riskier assets and miss out on higher long-term returns. Recently, advances in uncertainty visualizations have shown improvements in decision-making under uncertainty in a variety of tasks. In this paper, we conduct a controlled and incentivized crowdsourced experiment replicating Benartzi and Thaler (1999) and extending it to measure the effect of different uncertainty representations on myopic loss aversion. Consistent with the original study, we find evidence of myopic loss aversion with bar charts and find that participants make better investment decisions with longer evaluation periods. We also find that common uncertainty representations such as interval plots and bar charts achieve the highest mean expected returns while other uncertainty visualizations lead to poorer long-term performance and strong effects on the equity premium. Qualitative feedback further suggests that different uncertainty representations lead to visual reasoning heuristics that can either mitigate or encourage a focus on potential short-term losses. We discuss implications of our results on using uncertainty visualizations for retirement decisions in practice and possible extensions for future work.

Myopia drives reckless behavior in response to over-taxation

Governments use taxes to discourage undesired behaviors and encourage desired ones. One target of such interventions is reckless behavior, such as texting while driving, which in most cases is harmless but sometimes leads to catastrophic outcomes. Past research has demonstrated how interventions can backfire when the tax on one reckless behavior is set too high whereas other less attractive reckless actions remain untaxed. In the context of experience-based decisions, this undesirable outcome arises from people behaving as if they underweighted rare events, which according to a popular theoretical account can result from basing decisions on a small, random sample of past experiences. Here, we reevaluate the adverse effect of overtaxation using an alternative account focused on recency. We show that a reinforcement-learning model that weights recently observed outcomes more strongly than than those observed in the past can provide an equally good account of people’s behavior. Furthermore, we show that there exist two groups of individuals who show qualitatively distinct patterns of behavior in response to the experience of catastrophic outcomes. We conclude that targeted interventions tailored for a small group of myopic individuals who disregard catastrophic outcomes soon after they have been experienced can be nearly as effective as an omnibus intervention based on taxation that affects everyone.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.

Decisions from experience: Competitive search and choice in kind and wicked environments

Information search is key to making decision from experience: exploration permits people to learn about the statistical properties of choice options and thus to become aware of rare but potentially momentous decision consequences. This registered report investigates whether and how people differ when making decisions from experience in isolation versus under competitive pressure, which may have important implications for choice performance in different types of choice environments: in “kind” environments without any rare and extreme events, frugal search is sufficient to identify advantageous options. Conversely, in “wicked” environments with skewed outcome distributions, rare but important events will tend to be missed in frugal search. One theoretical view is that competitive pressure encourages efficiency and may thereby boost adaptive search in different environments. An alternative and more pessimistic view is that competitive pressure triggers agency-related concerns, leading to minimal search irrespective of the choice environment, and hence to inferior choice performance. Using a sampling game, the present study (N = 277) found that solitary search was not adaptive to different choice environments (M = 14 samples), leading to a high choice performance in a kind and in a moderately wicked environment, but somewhat lower performance in an extremely wicked environment. Competitive pressure substantially reduced search irrespective of the choice environment (M = 4 samples), thus negatively affecting overall choice performance. Yet, from the perspective of a cost-benefit framework, frugal search may be efficient under competitive pressure. In sum, this report extends research on decisions from experience by adopting an ecological perspective (i.e., systematically varying different choice environments) and by introducing a cost-benefit framework to evaluate solitary and competitive search — with the latter constituting a challenging problem for people in an increasingly connected world.

A tutorial on DynaSearch: A Web-based system for collecting process-tracing data in dynamic decision tasks

This tutorial describes DynaSearch, a Web-based system that supports process-tracing experiments on coupled-system dynamic decision-making tasks. A major need in these tasks is to examine the process by which decision makers search over a succession of situation reports for the information they need in order to make response decisions. DynaSearch provides researchers with the ability to construct and administer Web-based experiments containing both between- and within-subjects factors. Information search pages record participants' acquisition of verbal, numeric, and graphic information. Questionnaire pages query participants' recall of information, inferences from that information, and decisions about appropriate response actions. Experimenters can access this information in an online viewer to verify satisfactory task completion and can download the data in comma-separated text files that can be imported into statistical analysis packages.

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Age and Adaptation: Stronger Decision Updating about Real World Risks in Older Age

In later life, people are faced with a multitude of risky decisions that concern their health, finance, and personal security. Older adults often exercise caution in situations that involve risk. In this research, we asked whether older adults are also more responsive to warnings about potential risk. An answer to this question could reveal a factor underlying increased cautiousness in older age. In Study 1, participants decided whether they would engage in risky activities (e.g., using an ATM machine in the street) in four realistic scenarios about which participants could be expected to have relevant knowledge or experience. They then made posterior decisions after listening to audio extracts of real reports relevant to each activity. In Study 2, we explored the role that emotions play in decision updating. As in Study 1, participants made prior and posterior decisions, with the exception that for each scenario the reports were presented in their original audio format (high emotive) or in a written transcript format (low emotive). Following each posterior decision, participants indicated their emotional valence and arousal responses to the reports. In both studies, older adults engaged in fewer risky activities than younger adults, indicative of increased cautiousness in older age, and exhibited stronger decision updating in response to the reports. Older adults also showed stronger emotional responses to the reports, even though emotional responses did not differ for audio and written transcript formats. Finally, age differences in emotional responses to the reports accounted for age differences in decision updating.

The role of memory in distinguishing risky decisions from experience and description

People's risk preferences differ for choices based on described probabilities versus those based on information learned through experience. For decisions from description, people are typically more risk averse for gains than for losses. In contrast, for decisions from experience, people are sometimes more risk seeking for gains than losses, especially for choices with the possibility of extreme outcomes (big wins or big losses), which are systematically overweighed in memory. Using a within-subject design, this study evaluated whether this memory bias plays a role in the differences in risky choice between description and experience. As in previous studies, people were more risk seeking for losses than for gains in description but showed the opposite pattern in experience. People also more readily remembered the extreme outcomes and judged them as having occurred more frequently. These memory biases correlated with risk preferences in decisions from experience but not in decisions from description. These results suggest that systematic memory biases may be responsible for some of the differences in risk preference across description and experience.

The persistence of common-ratio effects in multiple-play decisions

People often make more rational choices between monetary prospects when their choices will be played out many times rather than just once. For example, previous research has shown that the certainty effect and the possibility effect (two common-ratio effects that violate expected utility theory) are eliminated in multiple-play decisions. This finding is challenged by seven new studies (N = 2391) and two small meta-analyses. Results indicate that, on average, certainty and possibility effects are reduced but not eliminated in multiple-play decisions. Moreover, in our within-participants studies, the certainty and possibility choice patterns almost always remained the modal or majority patterns. Our primary results were not reliably affected by prompts that encouraged a long-run perspective, by participants’ insight into long-run payoffs, or by participants’ numeracy. The persistence of common-ratio effects suggests that the oft-cited benefits of multiple plays for the rationality of decision makers’ choices may be smaller than previously realized.