Identification of Preferences in Forced-Choice Conjoint Experiments: Reassessing the Quantity of Interest

Forced-choice conjoint experiments have become a standard component of the experimental toolbox in political science and sociology. Yet the literature has largely overlooked the fact that conjoint experiments can be used for two distinct purposes: to uncover respondents’ multidimensional preferences, and to estimate the causal effects of some attributes on a profile’s selection probability in a multidimensional choice setting. This paper makes the argument that this distinction is both analytically and practically relevant, because the quantity of interest is contingent on the purpose of the study. The vast majority of social scientists relying on conjoint analyses, including most scholars interested in studying preferences, have adopted the average marginal component effect (AMCE) as their main quantity of interest. The paper shows that the AMCE is neither conceptually nor practically suited to explore respondents’ preferences. Not only is it essentially a causal quantity conceptually at odds with the goal of describing patterns of preferences, but it also does generally not identify preferences, mixing them with compositional effects unrelated to preferences. This paper proposes a novel estimand—the average component preference—designed to explore patterns of preferences, and it presents a method for estimating it.

The effect of nudges on autonomy in hypothetical and real life settings

Nudges have repeatedly been found to be effective, however they are claimed to harm autonomy, and it has been found that laypeople expect this too. To test whether these expectations translate to actual harm to experienced autonomy, three online studies were conducted. The paradigm used in all studies was that participants were asked to voluntarily participate in a longer version of the questionnaire. This was either done in a hypothetical setting, where participants imagined they were asked this question, but did not answer it, and reported their expectations for autonomy; Or in an actual choice setting where participants answered the question and then reported their actual autonomy. The first study utilized the hypothetical setting and tried to replicate that laypeople expect nudges to harm autonomy with the current paradigm. A total of 451 participants were randomly assigned to either a control, a default nudge, or a social norm nudge condition. In the default nudge condition, the affirmative answer was pre-selected, and in the social norm nudge condition it was stated that most people answered affirmative. The results showed a trend for lower expected autonomy in nudge conditions, but did not find significant evidence. In Study 2, with a sample size of 454, the same design was used in an actual choice setting. Only the default nudge was found to be effective, and no difference in autonomy was found. In Study 3, Studies 1 and 2 were replicated. Explanation of the nudge was added as an independent variable and the social norm nudge condition was dropped, resulting in six conditions and 1322 participants. The results showed that participants indeed expected default nudges to harm their autonomy, but only if the nudge was explained. When actually nudged, no effect on autonomy was found, independent of the presence of an explanation.

Virtual reality in specialist palliative care: a feasibility study to enable clinical practice adoption

BackgroundThe use of virtual reality (VR) is increasing in palliative care. However, despite increasing interest in VR, there is little evidence of how this technology can be implemented into practice.AimsThis paper aims to: (1) explore the feasibility of implementing VR therapy, for patients and caregivers, in a hospital specialist inpatient palliative care unit and a hospice, and (2) to identify questions for organisations, to support VR adoption in palliative care.MethodsThe Samsung Gear VR system was used in a hospital specialist palliative inpatient unit and a hospice. Patients and caregivers received VR distraction therapy and provided feedback of their experience. Staff completed a feedback questionnaire to explore their opinion of the usefulness of VR in palliative care. A public engagement event was conducted, to identify questions to support implementation of VR in palliative care settings.ResultsFifteen individuals (12 (80%) patients and 3 (20%) caregivers) participated. All had a positive experience. No adverse effects were reported. Ten items were identified for organisations to consider ahead of adoption of VR in palliative care. These were questions about: the purpose of VR; intended population; supporting evidence; session duration; equipment choice; infection control issues; content choice; setting of VR; person(s) responsible for delivery and the maintenance plan.ConclusionsIt is feasible to use VR therapy in palliative care; however, further evidence about its efficacy and effectiveness is needed. Palliative care practitioners considering VR use should carefully consider several factors, to ensure that this technology can be used safely and effectively in clinical practice.

Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats

Investigating the decision-making mechanisms underlying choice between drug and nondrug rewards is essential to understand how their alterations can contribute to substance use disorders. However, despite some recent effort, this investigation remains a challenge in a drug choice setting, notably when it comes to delineate the role of goal-directed versus habitual control mechanisms. The goal of this study was to try probing these different mechanisms by comparing response latencies measured during sampling (i.e., only one option is available) and choice trials. A deliberative goal-directed control mechanism predicts a lengthening of latencies during choice whereas a habitual control mechanism predicts no change in latencies. Alternatively, a race-like response competition mechanism, such as that postulated by the behavioral ecology-inspired Sequential Choice Model (SCM), predicts instead a shortening of response latencies during choice compared to sampling. Here we tested the predictions of these different mechanisms by conducting a systematic retrospective analysis of all cocaine versus saccharin choice experiments conducted in rats in our laboratory over the past 12 years. Overall, we found that rats engage a deliberative goal-directed mechanism after limited training, but shift to a SCM-like response selection mechanism after more extended training. The latter finding suggests that habitual control is engaged in a choice setting via a race-like response competition mechanism, and thus, that the SCM is not a general model of choice, as formulated initially, but a specific model of habitual choice.

Extending the Choice Architecture Toolbox: The Choice Context Mapping

The role of context in behavioral interventions is indisputable, yet few intervention studies start with a systematic mapping of the influencing contextual factors. This is mostly due to the lack of a methodology that researchers can employ for this aim. Recognizing this current limitation of the field, we developed a procedure, the Choice Context Mapping, to provide researchers a tool to examine the contextual factors of a targeted behavior. We demonstrate the steps of Choice Context Mapping on the behavioural choice situation of stairs vs. elevator use. Potential contextual factors were collected from laypeople as well as experts, and two surveys were created to measure both the behavior and choice, as well as the beliefs of participants. We estimated the effect of contextual factors on the participants’ behavior and managed to identify the most influential ones regarding their contribution to the studied choice. Using Choice Context Mapping, we obtained an accurate prediction of whether one chooses the stairs or elevator based on contextual information in 93.26% of the cases, on previously unseen data. We also ascertained that participants differed in their beliefs about what influences them in this particular choice, and that they can be divided into different groups based on their beliefs. Our results indicate that Choice Context Mapping is a useful procedure for the collection and assessment of contextual factors in a given choice setting which can help the planning of behavioral interventions by considerably lowering the number of possible interventions that are plausibly effective.

Neural Attribute Normalization: An Application to Product Portfolio Optimization

The importance of the choice set composition on individual preferences has been of wide interest in decision models. We propose a model seeking to elaborate on the role that choice set composition plays in a discrete choice problem through a normalization of the perceived value of each product's attributes. Our model extends the comprehension of context effects beyond the classical three-option cases of decoy, compromise and similarity. We apply a state-of-the-art class of models stemming from recent research on neural normalization to a multi-attribute choice setting. We also investigate the construction of the reference point by comparing different models, from simple cases to flexible neural networks. We highlight the performance of the model with an experimental application to credit card choices,and discuss the implications for product portfolio optimization. We find decisive evidence for attribute-based normalizing behavior. Understanding this normalization phenomenon will allow firms to optimize their portfolio with options whose main purpose is to increase the sales of their other products.

Identification of Preferences in Forced-Choice Conjoint Experiments: Reassessing the Quantity of Interest

Forced-choice conjoint experiments have become a standard element of the experimental toolbox in political science and sociology. Yet the literature has largely overlooked the fact that conjoint experiments are used for two distinct purposes: to uncover respondents' multidimensional preferences, and to estimate the causal effects of some attributes on a profile's selection probability in a multidimensional choice setting. This paper makes the argument that this distinction is both analytically and practically relevant, because the quantity of interest is contingent on the purpose of the study. The vast majority of social scientists relying on conjoint analyses, including most scholars interested in studying preferences, have adopted the average marginal component effect (AMCE) as their main quantity of interest. The paper shows that the AMCE is neither conceptually nor practically suited to explore respondents' preferences. Not only is it essentially a causal quantity conceptually at odds with the goal of describing patterns of preferences, but it also does generally not identify preferences, mixing them with compositional effects unrelated to preferences. This paper proposes a novel estimand—the average component preference (ACP)—designed to explore patterns of preferences, and it presents a method for estimating it.

Biased belief updating and suboptimal choice in foraging decisions

In many choice scenarios, including prey, employment, and mate search, options are not encountered simultaneously and so cannot be directly compared. Deciding which ones optimally to engage, and which to forego, requires developing accurate beliefs about the overall distribution of prospects. However, the role of learning in this process – and how biases due to learning may affect choice – are poorly understood. In three experiments, we adapted a classic prey selection task from foraging theory to examine how individuals kept track of an environment’s reward rate and adjusted their choices in response to its fluctuations. In accord with qualitative predictions from optimal foraging models, participants adjusted their selectivity to the richness of the environment: becoming less selective in poorer environments and increasing acceptance of less profitable options. These preference shifts were observed not just in response to global (between block) manipulations of the offer distributions, but also to local, trial-by-trial offer variation within a block, suggesting an incremental learning rule. Further offering evidence into the learning process, these preference changes were more pronounced when the environment improved compared to when it deteriorated. All these observations were best explained by a trial-by-trial learning model in which participants estimate the overall reward rate, but with upward vs. downward changes controlled by separate learning rates. A failure to adjust expectations sufficiently when an environment becomes worse leads to suboptimal choices: options that are valuable given the environmental conditions are rejected in the false expectation that better options will materialize. These findings offer a previously unappreciated parallel in the serial choice setting of observations of asymmetric updating and resulting biased (often overoptimistic) estimates in other domains.