How will an individual choose between different options?


Buying a second-hand car at a good price feels good. But choosing a delicious-looking doughnut in the supermarket leaves us riddled with doubt. After all, we resolved to eat a healthier diet this year – so wouldn’t it be better to buy an apple?

We’ve all experienced this feeling at one time or another: some decisions intuitively feel right, while others leave us feeling doubtful and may even cause us to revise our initial choice. But where does this feeling come from?

For the first time, a team of researchers at ETH Zurich and the University of Zurich led by ETH Professor Rafael Polanía has investigated this question systematically. The authors used experimental data to develop a computer model that can predict how an individual will choose between different options and why they might subsequently feel confident or doubtful about the decision they made.

“Using our model, we’ve successfully shown that decisions are most likely to feel right if we have invested significant attentional effort in weighing up the different options and, what’s more, are conscious of having done so,” says Polanía, who heads up the Decision Neuroscience Lab at ETH Zurich.

Consequently, the ability to question and revise poor decisions depends on how well we are able to judge for ourselves whether we thoroughly weighed up the options or allowed ourselves to be distracted during the decision-making process. This self-awareness, which experts typically refer to as introspection, is an essential prerequisite for self-control.

Examining subjective evaluations of choice in the lab

The confidence we have in our own decisions is based on subjective value estimations that we typically make automatically and unquestioningly as part of our day-to-day lives. To enable a systematic analysis of how this process works, Polanía and his team studied how test subjects evaluate and select everyday foods.

The 35 study participants were initially asked to evaluate 64 products from two Swiss supermarket chains. They were presented with a picture of each product on screen and asked how much they would like to eat it at the end of the experiment. In the second part of the experiment, the test subjects were shown a series of pictures that showed two products at the same time. In each case, they were asked to choose one of the two options – doughnut or apple, pizza or pear – and then rate how much confidence they had in their decision.

To make the experiment as realistic as possible, the participants had to eat the products after the experiment. The researchers used an eye scanner during both the evaluation and decision-making phases to determine whether the participants spent longer looking at one of the two products, how often their gaze shifted from left to right, and how quickly they made their decision.

Higher attentional effort leads to greater confidence

Using this data and a similar dataset from a different research group, Polanía together with his Ph.D. student Jeroen Brus developed a computer model that can predict under which conditions people will have confidence—or a lack thereof—in their decisions. “We discovered that people are particularly likely to have a bad feeling about a decision if they introspect that they didn’t pay enough attention to comparing the different options,” Polanía says.

The model uses the patterns of participants’ eye movements to determine how much effort they actually put into evaluating and comparing the different products. Someone who takes their time and always keeps both options in their sights is considered to have invested high attentional effort, while those who tend to fixate on just one option and neglect the other are regarded as having been less attentive.

The best way to illustrate these findings is by considering an example from everyday life: if we unthinkingly add a doughnut to our shopping basket, even after expressing an intention to eat more healthily, and subsequently realize that we didn’t even think about healthier alternatives, we ought to have low confidence in our decision and revise it. If, on the other hand, we are conscious of having carefully considered a series of healthier products but then decided against them because we simply wanted the doughnut more than an apple or pear, we should have confidence in our decision.

Using introspection to revise poor decisions

According to the study’s authors, the ability to question poor decisions and have confidence in good ones depends to a large extent on how conscious an individual is of their subjective value judgements and comparisons after making a decision. This is something neuroscientists refer to as introspection.

“Once we’ve made a decision, we can feel doubtful as to its value and revise it only if we’re actually conscious of the fact that we failed to pay enough attention to comparing the options,” Polanía says. This capacity for introspection is also a crucial part of our ability to exercise self-control. Without it, Polanía says, we would be far more likely to act on our preferences for, say, unhealthy foods without questioning them. The good news is that we can train this ability through mindfulness exercises and meditation.

Applications in smart glasses and self-driving vehicles

Polanía says this model could eventually be incorporated into smart glasses that track eye movements. “The glasses could use the model to determine how attentive we’re being and let us know when we should question a decision,” he says.

Polanía also believes the model could be useful for self-driving cars. The algorithms used in autonomous vehicles are constantly making decisions based on a continuous stream of data from the vehicle’s sensors. “Our model could help the vehicle evaluate its decisions and revise them where necessary,” Polanía says.


To understand how sociology and psychology offer distinct but complementary views of decision processes, we begin with a brief introduction to the dominant model of human decision-making in the social sciences: rational choice theory. This model, endemic to neoclassical economic analyses, has permeated into many fields including sociology, anthropology, political science, philosophy, history, and law (Coleman 1991; Gely & Spiller 1990; Satz & Ferejohn 1994; Levy 1997).

In its classic form, the rational choice model of behavior assumes that decision makers have full knowledge of the relevant aspects of their environment, a stable set of preferences for evaluating choice alternatives, and unlimited skill in computation (Samuelson 1947; Von Neumann & Morgenstern 2007; Becker 1993). Actors are assumed to have a complete inventory of possible alternatives of action; there is no allowance for focus of attention or a search for new alternatives (Simon 1991, p. 4). Indeed, a distinguishing feature of the classic model is its lack of attention to the process of decision-making. Preference maximization is a synonym for choice (McFadden 2001, p. 77).

Rational choice has a long tradition in sociology, but its popularity increased in the 1980s and 1990s, partly as a response to concern within sociology about the growing gap between social theory and quantitative empirical research (Coleman 1986). Quantitative data analysis, despite focusing primarily on individual-level outcomes, is typically conducted without any reference to—let alone a model of—individual action (Goldthorpe 1996; Esser 1996). Rational choice provides a theory of action that can anchor empirical research in meaningful descriptions of individuals’ behavior (Hedström & Swedberg 1996). Importantly, the choice behavior of rational actors can also be straightforwardly implemented in regression-based models readily available in statistical software packages. Indeed while some scholars explicitly embrace rational choice as a model of behavior (Hechter and Kanazawa 1997; Kroneberg and Kalter 2012), many others implicitly adopt it in their quantitative models of individual behavior.

Beyond Rational Choice

Sociologists have critiqued and extended the classical rational choice model in a number of ways. They have observed that people are not always selfish actors who behave in their own best interests (England 1989; Margolis 1982), that preferences are not fixed characteristics of individuals (Lindenberg and Frey 1993; Munch 1992), and that individuals do not always behave in ways that are purposive or optimal (Somers 1989; Vaughan 1998).

Most relevant to this article, sociologists have argued that the focus in classical rational choice on the individual as the primary unit of decision-making represents a fundamentally asocial representation of behavior. In moving beyond rational choice, theories of decision-making in sociology highlight the importance of social interactions and relationships in shaping behavior (Pescosolido 1992; Emirbayer 1997). A large body of empirical work reveals how social context shapes people’s behavior across a wide range of domains, from neighborhood and school choice to decisions about friendship and intimacy to choices about eating, drinking, and other health-related behaviors (Carrillo et al. 2016; Perna and Titus 2005; Small 2009; Pachucki et al. 2011; Rosenquist et al. 2010).

But This focus on social environments and social interactions has inevitably led to less attention being paid to the individual-level processes that underlie decision-making. In contrast, psychologists and decision theorists aiming to move beyond rational choice have focused their attention squarely on how individuals make decisions. In doing so, they have amassed several decades of work showing that the rational choice model is a poor representation of this process.4

Their fundamental critique is that decision-making, as envisioned in the rational choice paradigm, would make overwhelming demands on our capacity to process information (Bettman 1979; Miller 1956; Payne 1976). Decision-makers have limited time for learning about choice alternatives, limited working memory, and limited computational capabilities (Miller 1956; Payne et al.1993) As a result, they use heuristics that keep the information-processing demands of a task within the bounds of their limited cognitive capacity.5 It is now widely recognized that the central process in human problem solving is to apply heuristics that carry out highly selective navigations of problem spaces (Newell & Simon 1972).

However, in their efforts to zero in on the strategies people use to gather and process information, psychological studies of decision-making have focused largely on individuals in isolation. Thus, sociological and psychological perspectives on choice are complementary in that they each emphasize a feature of decision-making that the other field has left largely undeveloped. For this reason, and as we articulate further in the conclusion, we believe there is great potential for cross-fertilization between these areas of research. Because our central aim is to introduce sociologists to the JDM literature, we do not provide an exhaustive discussion of sociological work relevant to understanding decision processes. Rather, we highlight studies that illustrate the fruitful connections between sociological concerns and JDM research.

In the next sections, we discuss the role of different factors—cognitive, emotional, and contextual—in heuristic decision processes.


There are two major challenges in processing decision-related information: first, each choice is typically characterized by multiple attributes, and no alternative is optimal on all dimensions; and, second, more than a tiny handful of information can overwhelm the cognitive capacity of decision makers (Cowan 2010). Consider the problem of choosing among three competing job offers. Job 1 has high salary, but a moderate commuting time and a family-unfriendly workplace. Job 2 offers a low salary, but has a family-friendly workplace and short commuting time. Job 3 has a family-friendly workplace but a moderate salary and long commuting time.

This choice would be easy if one alternative clearly dominated on all attributes. But, as is often the case, they all involve making tradeoffs and require the decision maker to weigh the relative importance of each attribute. Now imagine that, instead of three choices, there were ten, a hundred, or even a thousand potential alternatives. This illustrates the cognitive challenge faced by people trying to decide among neighborhoods, potential romantic partners, job opportunities, or health care plans.

We focus in this section on choices that involve deliberation, for example deciding where to live, what major to pursue in college, or what jobs to apply for.6 (This is in contrast to decisions that are made more spontaneously, such as the choice to disclose personal information to a confidant [Small & Sukhu 2016].) Commencing with the pioneering work of Howard & Sheth (1969), scholars have accumulated substantial empirical evidence for the idea that such decisions are typically made sequentially, with each stage reducing the set of potential options (Swait 1984; Roberts & Lattin 1991, 1997).

For a given individual, the set of potential options can first be divided into the set that he or she knows about, and those of which he or she is unaware. This “awareness set” is further divided into options the person would consider, and those that are irrelevant or unattainable. This smaller set is referred to as the consideration set, and the final decision is restricted to options within that set.

Research in consumer behavior suggests that the decision to include certain alternatives in the consideration set can be based on markedly different heuristics and criteria than the final choice decision (e.g., Payne 1976; Bettman & Park 1980; Salisbury & Feinberg 2012). In many cases, people use simple rules to restrict the energy involved in searching for options, or to eliminate options from future consideration.

For example, a high school student applying to college may only consider schools within commuting distance of home, or schools where someone she knows has attended. Essentially, people favor less cognitively taxing rules that use a small number of choice attributes earlier in the decision process to eliminate almost all potential alternatives, but take into account a wider range of choice attributes when evaluating the few remaining alternatives for the final decision (Liu & Dukes 2013).

Once the decision maker has narrowed down his or her options, the final choice decision may allow different dimensions of alternatives to be compensatory; in other words, a less attractive value on one attribute may be offset by a more attractive value on another attribute. However, a large body of decision research demonstrates that strategies to screen potential options for consideration are non-compensatory; a decision-maker’s choice to eliminate from or include for consideration based on one attribute will not be compensated by the value of other attributes. In other words, compensatory decision rules are “continuous,” while non-compensatory decision rules are discontinuous or threshold (Swait 2001; Gilbride & Allenby 2004).

Compensatory Decision Rules

The implicit decision rule used in statistical models of individual choice and the normative decision rule for rational choice is the weighted additive rule. Under this choice regime, decision-makers compute a weighted sum of all relevant attributes of potential alternatives. Choosers develop an overall assessment of each choice alternative by multiplying the attribute weight by the attribute level (for each salient attribute), and then sum over all attributes.

This produces a single utility value for each alternative. The alternative with the highest value is selected, by assumption. Any conflict in values is assumed to be confronted and resolved by explicitly considering the extent to which one is willing to trade off attribute values, as reflected by the relative importance or beta coefficients (Payne et al. 1993, p. 24). Using this rule involves substantial computational effort and processing of information.

A simpler compensatory decision rule is the tallying rule, known to most of us as a “pro and con” list (Alba & Marmorstein 1987). This strategy ignores information about the relative importance of each attribute. To implement this heuristic, a decision maker decides which attribute values are desirable or undesirable. Then she counts up the number desirable versus undesirable attributes. Strictly speaking, this rule forces people to make trade-offs among different attributes. However, it is less cognitively demanding than the weighted additive rule, as it does not require people to specify precise weights associated with each attribute. But both rules require people to examine all information for each alternative, determine the sums associated with each alternative, and compare those sums.

Non-Compensatory Decision Rules

Non-compensatory decision rules do not require decision makers to explicitly consider all salient attributes of an alternative, assign numeric weights to each attribute, or compute weighted sums in one’s head. Thus they are far less cognitively taxing than compensatory rules. The decision maker need only examine the attributes that define cutoffs in order to make a decision (to exclude options for a conjunctive rule, or to include them for a disjunctive one). The fewer attributes that are used to evaluate a choice alternative, the less taxing the rule will be.

Conjunctive rules require that an alternative must be acceptable on one or more salient attributes. For example, in the context of residential choice, a house that is unaffordable will never be chosen, no matter how attractive it is. Similarly, a man looking for romantic partners on an online dating website may only search for women who are within a 25-mile radius and do not have children. Potential partners who are unacceptable on either dimension are eliminated from consideration. So conjunctive screening rules identify “deal-breakers”; being acceptable on all dimensions is a necessary but not sufficient criterion for being chosen.

A disjunctive rule dictates that an alternative is considered if at least one of its attributes is acceptable to chooser i. For example, a sociology department hiring committee may always interview candidates with four or more American Journal of Sociology publications, regardless of their teaching record or quality of recommendations. Similarly (an especially evocative yet somewhat fanciful example), a disjunctive rule might occur for the stereotypical “gold-digger” or “gigolo,” who targets all potential mates with very high incomes regardless of their other qualities. Disjunctive heuristics are also known as “take-the-best” or “one good reason” heuristics that base their decision on a single overriding factor, ignoring all other attributes of decision outcomes (Gigerenzer & Goldstein 1999; Gigerenzer & Gaissmaier 2011; Gigerenzer 2008).

While sociologists studying various forms of deliberative choice do not typically identify the decision rules used, a handful of empirical studies demonstrate that people do not consider all salient attributes of all potential choice alternatives. For example, Krysan and Bader (2009) find that white Chicago residents have pronounced neighborhood “blind spots” that essentially restrict their knowledge of the city to a small number of ethnically homogeneous neighborhoods.

Daws and Brown (2002 Daws and Brown (2004) find that, when choosing a college, UK students’ awareness and choice sets differ systematically by socioeconomic status. Finally, in a recent study of online mate choice, Bruch and colleagues (2016) build on insights from marketing and decision research to develop a statistical model that allows for multistage decision processes with different (potentially noncompensatory) decision rules at each stage. They find that conjunctive screeners are common at the initial stage of online mate pursuit, and precise cutoffs differ by gender and other factors.


Early decision research emphasized the role of cognitive processes in decision-making (e.g., Newell & Simon 1972). But more recent work shows that emotions—not just strong emotions like anger and fear, but also “faint whispers of emotions” known as affect (Slovic et al. 2004, p. 312)—play an important role in decision-making. Decisions are cast with a certain valence, and this shapes the choice process on both conscious and unconscious levels. In other words, even seemingly deliberative decisions, like what school to attend or job to take, may be made not just through careful processing of information, but based on intuitive judgments of how a particular outcome feels (Loewenstein & Lerner 2003; Lerner et al. 2015).

This is true even in situations where there is numeric information about the likelihood of certain events (Denes-Raj & Epstein 1994; Windschitl & Weber 1999; Slovic et al. 2000). This section focuses on two topics central to this area: first, that people dislike making emotional tradeoffs, and will go to great lengths to avoid them; and second, how emotional factors serve as direct inputs into decision processes.7

Emotions Shape Strategies for Processing Information

In the previous section, we emphasized that compensatory decision rules that involve tradeoffs require a great deal of cognitive effort. But there are other reasons why people avoid making explicit tradeoffs on choice attributes. For one, some tradeoffs are more emotionally difficult than others, for example the decision whether to stay at home with one’s children or put them in day care. Some choices also involve attributes that are considered sacred or protected (Baron & Spranca 1997).

People prefer not to make these emotionally difficult tradeoffs, and that shapes decision strategy selection (Hogarth 1991; Baron 1986; Baron & Spranca 1997). Experiments on these types of emotional decisions have shown that, when facing emotionally difficult decisions, decision-makers avoid compensatory evaluation and instead select the alternative that is most attractive on whatever dimension is difficult to trade off (Luce et al. 2001; Luce et al. 1999). Thus, the emotional valence of specific options shapes decision strategies.

Emotions concerning the set of all choice alternatives—specifically, whether they are perceived as overall favorable or unfavorable—also affects strategy selection. Early work with rats suggests that decisions are relatively easy when choosing between two desirable options with no downsides (Miller 1959). However, when deciding between options with both desirable and undesirable attributes, the choice becomes harder. When deciding between two undesirable options, the choice is hardest of all. Subsequent work reveals that this finding extends to human choice.

For instance, people invoke different choice strategies when forced to choose “the lesser of two evils.” In their experiments on housing choice, Luce and colleagues (2000) found that when faced with a set of substandard options, people are far more likely to engage in “maximizing” behavior and select the alternative with the best value on whatever is perceived as the dominant substandard feature. In other words, having a suboptimal choice set reduces the likelihood of tradeoffs on multiple attributes. Extending this idea to a different sociological context, a woman confronted with a dating pool filled with what she perceives as arrogant men may focus her attention on selecting the least arrogant of the group.

Emotions as Information

Emotions also serve as direct inputs into the decision process. A large body of work on perceptions of risk shows that a key way people evaluate the risks and benefits of a given situation is through their emotional response (Slovic et al. 2004; Slovic and Peters 2006; Loewenstein et al. 2001). In a foundational and generative study, Fischhoff et al. (1978) discovered that people’s perceptions of risks decline as perceived benefits increase.

This is puzzling, because risks and benefits tend to be positively correlated. The authors also noted that the attribute most highly correlated with perceived risk was the extent to which the item in question evoked a feeling of dread. This finding has been confirmed in many other studies (e.g., McDaniels et al. 1997). Subsequent work also showed that this inverse relationship is linked to the strength of positive or negative affect associated with the stimulus. In other words, stronger negative responses led to perception of greater risk and lower benefits (Alhakami & Slovic 1994; Slovic & Peters 2006).

This has led to a large body of work on the affect heuristic, which is grounded in the idea that people have positive and negative associations with different stimuli, and they consult this “affect pool” when making judgments. This shortcut is often more efficient and easier than cognitive strategies such as weighing pros and cons or even disjunctive rules for evaluating the relative merits of each choice outcome (Slovic et al. 2004).

Affect— particularly how it relates to decision-making—is rooted in dual process accounts of human behavior. The basic idea is that people experience the world in two different ways: one that is fast, intuitive, automatic, and unconscious, and another that is slow, analytical, deliberate, and verbal (Evans 2008; Kahneman 2011). A defining characteristic of the intuitive, automatic system is its affective basis (Epstein 1994). Indeed, affective reactions to stimuli are often the very first reactions people have. Having determined what is salient in a given situation, affect thus guides subsequent processes, such as information processing, that are central to cognition (Zajonc 1980).

Over the past two decades, sociologists—particularly in the study of culture—have incorporated insights from dual process theory to understand how actions may be both deliberate and automatic (e.g., Vaisey 2009). Small and Sukhu (2016) argue that dual processes may play an important role in the mobilization of support networks. Kroneberg and Esser (Kroneberg 2014; Esser and Kroneberg 2015) explore how automatic and deliberative processes shape how people select the “frame” for making sense of a particular situation. Although some scholars debate whether automatic and deliberative processes are more like polar extremes or a smooth spectrum (for an example of this critique within sociology, see Leschziner and Green 2013), the dual process model remains a useful framework for theorizing about behavior.

reference link:

More information: Jeroen Brus et al, Sources of confidence in value-based choice, Nature Communications (2021). DOI: 10.1038/s41467-021-27618-5


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