Choosing Less‐Preferred Experiences For the Sake of Variety

Diversification bias: Explaining the discrepancy in variety seeking between combined and separated choices.

Characterizing the causes, dynamics, and consequences of choice deferral

The fact that people often avoid making decisions is well known, and past research has helped to identify some of the conditions and reasons for doing so. For instance, people may forgo choices between bad options because they prefer not to end up with one of those options. It is much less clear when and why people avoid choosing in cases where they eventually will have to make a given decision. To study such instances of choice deferral, we presented participants with a series of choices, and for each choice they were allowed to either choose immediately or defer the decision until later in the experiment. Across six experiments and three choice domains (choices among consumer goods, artwork, and political candidates), we find that the strongest predictor of choice deferral is the overall value of a given set of options, with relative value (i.e., how hard it is to identify the best option) counterintuitively playing a smaller role. We show that the influence of overall value on choice deferral can be accounted for by a dynamic decision model according to which participants appraise the option set relative to a criterion before deciding whether to choose or defer, comparing this to a previous model whereby participants make such a decision based on a predetermined decision time limit. We further reveal that the influence of overall value on choice deferral is determined by how congruent options are with a given choice goal (choose-best or choose-worst) rather than simply how bad those options are. Collectively, our findings shed new light on how people decide to put off the inevitable.

A Behavioral Model of Rational Choice

Abstract. Introduction, 99. — I. Some general features of rational choice, 100.— II. The essential simplifications, 103. — III. Existence and uniqueness of

Anticipation and Choice Heuristics in the Dynamic Consumption of Pain Relief

Humans frequently need to allocate resources across multiple time-steps. Economic theory proposes that subjects do so according to a stable set of intertemporal preferences, but the computational demands of such decisions encourage the use of formally less competent heuristics. Few empirical studies have examined dynamic resource allocation decisions systematically. Here we conducted an experiment involving the dynamic consumption over approximately 15 minutes of a limited budget of relief from moderately painful stimuli. We had previously elicited the participants’ time preferences for the same painful stimuli in one-off choices, allowing us to assess self-consistency. Participants exhibited three characteristic behaviors: saving relief until the end, spreading relief across time, and early spending, of which the last was markedly less prominent. The likelihood that behavior was heuristic rather than normative is suggested by the weak correspondence between one-off and dynamic choices. We show that the consumption choices are consistent with a combination of simple heuristics involving early-spending, spreading or saving of relief until the end, with subjects predominantly exhibiting the last two.

Complexity and biases

We examine experimentally how complexity affects decision-making, when individuals choose among different products with varying benefits and costs. We find that complexity in costs leads to choosing a high-benefit product, with high costs and overall lower payoffs. In contrast, when complexity is in the benefits of the product, we cannot reject the hypothesis of random mistakes. We also examine the role of heterogeneous complexity. We find that individuals still (mistakenly) choose the high-benefit but costly product, even if cheaper and simple products are available. Our results suggest that salience is a main driver of choices under different forms of complexity.

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Online platforms have a wealth of data, run countless experiments, and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which wants to maximize user utility, but only observes behavioral data in the form of the user’s engagement. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go “cold turkey” and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: For certain directions of change, increasing engagement makes users less happy, whereas in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Vannevar Bush Faculty Fellowship and Multidisciplinary University Research Initiative [Grant W911NF-19-0217]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03683 .

Time-inconsistent Preferences and Consumer Self-Control

Abstract Why do consumers sometimes act against their own better judgment, engaging in behavior that is often regretted after the fact and that would have been rejected with adequate forethought? More generally, how do consumers attempt to maintain self-control in the face of time-inconsistent preferences? This article addresses consumer impatience by developing a decision-theoretic model based on reference points. The model explains how and why consumers experience sudden increases in desire for a product, increases that can result in the temporary overriding of long-term preferences. Tactics that consumers use to control their own behavior are also discussed. Consumer self-control is framed as a struggle between two psychological forces, desire and willpower. Finally, two general classes of self-control strategies are described: those that directly reduce desire, and those that overcome desire through will power.

Metacognitive experiences as information: Processing fluency in consumer judgment and decision making

Abstract Thinking is accompanied by metacognitive experiences of ease or difficulty. People draw on these experiences as a source of information that can complement or challenge the implications of declarative information. We conceptualize the operation of metacognitive experiences within the framework of feelings‐as‐information theory and review their implications for judgments relevant to consumer behavior, including popularity, trust, risk, truth, and beauty.

Metacognitive experiences as information: Processing fluency in consumer judgment and decision making

Abstract Thinking is accompanied by metacognitive experiences of ease or difficulty. People draw on these experiences as a source of information that can complement or challenge the implications of declarative information. We conceptualize the operation of metacognitive experiences within the framework of feelings‐as‐information theory and review their implications for judgments relevant to consumer behavior, including popularity, trust, risk, truth, and beauty.

Task-Dependent Algorithm Aversion

Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.

Task-Dependent Algorithm Aversion

Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.

The Ratchet: How Preference Standards Erase What They Can't Express - Astral's Blog

Choice Bracketing

When making many choices, a person can broadly bracket them by assessing the consequences of all of them taken together, or narrowly bracket them by making each choice in isolation. We integrate research conducted in a wide range of decision contexts which shows that choice bracketing is an important determinant of behavior. Because broad bracketing allows people to take into account all the consequences of their actions, it generally leads to choices that yield higher utility. The evidence that we review, however, shows that people often fail to bracket broadly when it would be feasible for them to do so. In addition to documenting the diverse effects of bracketing, we also discuss factors that determine whether people bracket narrowly or broadly. We conclude with a discussion of normative aspects of bracketing and argue that there are some situations in which narrower bracketing results in superior decision making.

How Targeting Affects Customer Search: A Field Experiment

It has become common practice for retailers to personalize direct marketing efforts based on customer transaction histories as a tactic to increase sales. Targeted email offers featuring products in the same category as a customer’s previous purchases generate higher purchase rates. However, a targeted offer emphasizing familiar products could result in curtailed search for unadvertised products, as a closely matched offer weakens a customer’s incentives to search beyond the targeted items. In a field experiment using email offers sent by an online wine retailer, targeted offers resulted in decreased search activity on the retailer’s website. This effect is driven by a lower rate of search by customers who visit the site, rather than a lower incidence of search. There are several ways this could potentially hurt retailers and consumers, such as reduced cross-selling and fewer opportunities for customers to explore new products. This paper was accepted by Pradeep Chintagunta, marketing.

How to know what you really want and be free from mimetic desire | Psyche Guides

From career choices to new purchases, use René Girard’s mimetic theory to resist the herd and forge your own path in life