Aggregate Choice Research Articles

Using neural data to forecast aggregate consumer behavior in neuromarketing: Theory, metrics, progress, and outlook

AbstractThe field of using neural data to forecast aggregate consumer choice has garnered attention in the past decade, holding substantial promise for both researchers and practitioners. However, a comprehensive understanding of this emerging field is lacking. This paper aims to bridge that gap by summarizing existing research, encompassing relevant theories, metrics, progress, and future directions. We begin by introducing the concept of neuroforecasting within the field of neuromarketing. We then delve into theories that leverage neural data for forecasting aggregate choice, including affect‐integration‐motivation framework, frontal asymmetry, and inter‐subject correlation. Subsequently, we review various metrics, including self‐reported, behavioral, and neural metrics employed to forecast market‐level behavior, presenting key findings from relevant studies. Furthermore, we examine the strengths and weaknesses of this field. Advantages of this approach include its ability to offer effective predictions of consumer behavior and provide enhanced insights into consumer preferences and choices, while its weaknesses encompass relatively high cost, sample size constraints, issues of ecological validity, and challenges related to reverse inference. In conclusion, future research should prioritize integrating diverse data types with machine learning techniques to forecast the outcomes of marketing campaigns in advance. Additionally, a deeper exploration of the psychological and cognitive processes underlying successful predictions can augment predictive accuracy and effectiveness. This review provides a systematic overview for researchers and practitioners in this field, offering valuable insights and guidance for future research endeavors and industry applications.

Emphasis of Cyclic Loading on the Fracture Mechanism and Residual Fracture Toughness of High-Performance Concrete Considering the Morphological Properties of Aggregate

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) under static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B’s remarkable superiority, displaying its higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, emphasizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and an enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and the displacement rate on crack formation. High-cycle fatigue tests show HPC-B’s superior performance, and the post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interaction. A 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This research marks a significant shift from conventional aggregate-focused approaches and introduces a novel approach by integrating excess paste theory and mesoscale analysis, highlighting the critical role of aggregate choice in material characterization and mesoscale design in enhancing the structural efficiency of HPC. Furthermore, the study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types and morphologies and their dynamic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

The prediction of market-level food choices by the neural valuation signal.

Neuroimaging studies have demonstrated the ability to use the brain activity of a group of individuals to forecast the behavior of an independent group. In the current study, we attempted to forecast aggregate choices in a popular restaurant chain. During our functional magnetic resonance imaging (fMRI) study, 22 participants were exposed to 78 photos of dishes from a new menu of a popular restaurant chain. In addition to self-reported preferences, fMRI data was extracted from an a priori domain-general and task-specific region of interest-the ventral striatum. We investigated the relationship between the neural activity and real one-year sales provided by the restaurant chain. Activity in the ventral striatum, which was defined using the task-specific region of interest, significantly correlated (r = 0.28, p = 0.01) with one-year sales. A regression analysis, which included ventral striatum activity together with the objective characteristics of the products (price and weight), behavioral, and survey data, showed R2 values of 0.33. Overall, our results confirm prior studies, which have suggested, that brain activity in the reward system of a relatively small number of individuals can forecast the aggregate choice of a larger independent group of people.

Modeling and Solving Line Planning with Mode Choice

We present a mixed-integer linear program (MILP) for line planning with mode and route choice. In contrast to existing approaches, the mode and route decisions of passengers are modeled depending on the line plan and commercial solvers can be applied to solve the corresponding MILP. The model aims at finding line plans that maximize the profit for the railway operator while estimating the corresponding passenger demand with aggregate choice models. Hence, the resulting line plans are not only profitable for operators but also attractive to passengers. By suitable preprocessing we are able to apply any aggregate choice model for mode choices using linear constraints. We provide and test means to improve the computational performance. Our experiments on the Intercity network of the Randstad, a metropolitan area in the Netherlands, show that models which assume a fixed passenger assignment to modes cannot attract the passenger numbers they were designed for and therefore lead to inferior profit compared with our new model. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1171 .

On the descriptive value of the reliance on small-samples assumption

AbstractExperience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.

Brain Activity Foreshadows Stock Price Dynamics.

Successful investing is challenging since stock prices are difficult to consistently forecast. Recent neuroimaging evidence suggests, however, that activity in brain regions associated with anticipatory affect may not only predict individual choice, but also forecast aggregate behavior out-of-sample. Thus, in two experiments, we specifically tested whether anticipatory affective brain activity in healthy humans could forecast aggregate changes in stock prices. Using functional magnetic resonance imaging, we found in a first experiment (n = 34, 6 females; 140 trials/subject) that nucleus accumbens activity forecast stock price direction, whereas anterior insula (AIns) activity forecast stock price inflections. In a second preregistered replication experiment (n = 39, 7 females) that included different subjects and stocks, AIns activity still forecast stock price inflections. Importantly, AIns activity forecast stock price movement even when choice behavior and conventional stock indicators did not (e.g., previous stock price movements), and classifier analysis indicated that forecasts based on brain activity should generalize to other markets. By demonstrating that AIns activity might serve as a leading indicator of stock price inflections, these findings imply that neural activity associated with anticipatory affect may extend to forecasting aggregate choice in dynamic and competitive environments such as stock markets.SIGNIFICANCE STATEMENT Many try but fail to consistently forecast changes in stock prices. New evidence, however, suggests that anticipatory affective brain activity may not only predict individual choice, but also may forecast aggregate choice. Assuming that stock prices index collective choice, we tested whether brain activity sampled during the assessment of stock prices could forecast subsequent changes in the prices of those stocks. In two neuroimaging experiments, a combination of previous stock price movements and brain activity in a region implicated in processing uncertainty and arousal forecast next-day stock price changes—even when behavior did not. These findings challenge traditional assumptions of market efficiency by implying that neuroimaging data might reveal “hidden information” capable of foreshadowing stock price dynamics.

ПОВЕДІНКА ІНДИВІДУАЛЬНИХ СПОЖИВАЧІВ ЯК ОБ’ЄКТ УПРАВЛІНСЬКИХ ІНТЕРЕСІВ

The article deals with the reasons, directions and scope of the interest of society, institutions of state regulation and managers of business structures concerning the behavior of households as consumers. It was demonstrated that, in the modern socioeconomic system, households act as independent agents, including the area of consumer behavior. At the same time, the consumer behavior of households has a socialized character: firstly, in the context of the implementation of the procedure for obtaining the goods needed to meet the needs; secondly, in the context of the emergence of certain social consequences from the use of acquired goods by households; thirdly, in the context of the conditions of existence, functioning and development of business organizations. The socialized nature of consumer activity determines that the behavior of individual consumers should be the point of interest and influence from the society, state and local institutions of the government and business. At the same time, that behavior can be the subject of interest and influence from the side of society, state institutions of management and business. It was proved that protection of public interests requires an establishment of the certain social control over consumer behavior. On the basis of the analysis it was established that the social interest in individual consumer behavior can be represented in the following directions (forms): objective, quantitative and subjective-territorial. It was found that the objective public interest in the consumer behavior of households involves the focus on consumer choice of specific goods and services. Assessment of consumer behavior in the context of “buying” is associated with the impact of such choices, firstly, on the health of individuals and society taken as a whole, and secondly, on the worldview, creative and intellectual development as individuals, and society taken as a whole. By contrast, the health condition affects the level of “social happiness” (social welfare), efficiency and productivity of social work (ability to perform a certain amount of work and provide a certain level of intensity), the corresponding costs of the state and local budgets connected with the prevention and treatment of diseases, the use of various compensation payments related to the health problems. Quantitative form (direction) of public interest in the behavior of individual consumers may be related to the health issues, macroeconomic policy, resource potential, ensuring a balanced situation in different commodity markets, achieving the required level of the national economic security. The subjective territorial direction of public interest in the market behavior of households is related to the assessment of the social consequences of individual consumer decisions on the choice of entities in the supply chain of goods and services. Such entities may be business organizations of a particular jurisdiction: residents or non-residents. The aggregate market choice of consumer households in this context has certain macroeconomic implications related to the competitiveness of the national economy, the current and future state of its security

Myopic reactivity in repeated-choice: The case of competing with others for a scarce, unevenly distributed resource.

Foraging for a scarce resource takes place when fewer resource-units than agents are distributed among several locations and agents choose at which location to look for the resource. But how do foragers distribute themselves over the different locations? Optimal foraging theory postulates that the distribution of agents should match the distribution of resource units (ideal free distribution [IFD]), but research with animals and humans has revealed undermatching at the location at which the resource is most abundant. For the IFD to be reached, full information about other foragers' choices and outcomes is required, information that is usually not available. We conducted a theoretical analysis of the implications of relying on different levels of information: on the incomplete, but still valid information usually available in foraging scenarios and on full information. The analysis demonstrates that myopic reactivity to disappointment, or to regret, which are likely to arise in the wake of incomplete information, leads to undermatching, with either affect leading to different degrees of undermatching. Importantly, these analyses indicate that behavior would be sensitive not only to resource distribution (as in IFD), but also to its overall abundance. Three experiments employing incentivized repeated choices were conducted. The information provided to participants, the number of locations, and resource abundance were manipulated to test the predictions of the models. Analyses of aggregate choice probabilities and trial-to-trial choice dynamics indicate that myopic reactivity to regret provides the best explanation for the observed data. With more information available, behavior matches more closely the IFD predictions. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Inferring Cognitive Heterogeneity From Aggregate Choices

Theories of bounded rationality often assume a rich dataset of choices from many overlapping menus, limiting their practical applicability. In contrast, we study the problem of identifying the distribution of cognitive characteristics in a population of agents from a minimal dataset that consists of aggregate choice shares from a single menu, and includes no observable covariates of any kind. With homogeneous preferences, we find that “consideration capacity” and “consideration probability” distributions can both be recovered effectively if the menu is sufficiently large. This remains true generically when tastes are heterogeneous with a known distribution. When the taste distribution is unknown, we show that joint choice share data from three “occasions” are generically sufficient for full identification of the cognitive distribution, and also provide substantial information about tastes.

Adolescents' and Parents' Genomic Testing Decisions: Associations With Age, Race, and Sex

PurposeAdolescents will increasingly be involved in decisions about the return of genomic results. We examined adolescents' and parents' decisions about learning actual genomic research results for the adolescent and whether choices were associated with participants' demographic factors. MethodsAdolescents aged between 13 and 17 years and a parent (dyads) were recruited through flyers, social media, employee emails, and clinic visits at a pediatric hospital. Dyads used a decision tool to independently choose the categories of conditions they wanted to learn about the adolescent. They then came together to discuss their independent decisions and make final joint decisions. Conditions were categorized by preventability, treatability, adult-onset conditions, and carrier status. Participants could make granular choices by including or excluding conditions in each category. Categorical choices were collapsed into the “aggregate choice” to learn all or not all results. ResultsStudy visits were completed by 163 dyads. Adolescents were less likely than their parents to independently choose to learn all results (64.4% vs. 76.1%; p = .0056). Parents were less likely to independently choose to learn all results for their daughters than their sons (odds ratio = .41, 95% confidence interval .18–.96; p = .032). Black adolescents were less likely to independently choose to learn all results than white adolescents (odds ratio = .22; 95% confidence interval .08–.55; p = .0015). After making joint decisions, 70.6% of dyads chose to learn all results. ConclusionsAdolescents independently wanted to learn less genomic information than their parents. Although adolescents cannot legally make genomic testing decisions without parental permission, adolescents' should be engaged in decisions about the return of genomic results.

The impact of rewarding medium effort and the role of sample size

AbstractWe take the point‐of‐view of designers of incentive systems who cannot reward the behavior they most desire, and must decide whether to reward a less desired behavior. For example, when it is difficult to distinguish between the desired high‐effort strategy from a low‐effort “mimicry” strategy, policy makers may choose instead to reward medium levels of effort. Experiments 1 and 2 examine situations in which participants (the subjects of the policy) base their decisions on personal experience obtained in 100 choices with immediate feedback. The results show that rewarding medium levels of effort is effective in "reckless shortcut" settings (where a low‐effort strategy impairs participants' expected return but provides the best payoff in most cases), and is counterproductive in "treasure‐hunt shortcut" settings (where a low‐effort strategy maximizes the expected return, but provides the worst payoff in most cases). Experiment 3 suggests that this pattern is intensified when participants base their decisions on the experience of other agents. The aggregate choice rates can be captured with a two‐stage naïve sampler model that assumes reliance on small samples of past experiences, and a decrease in the sample size when learning from the experience of others. Importantly, the descriptive value of the reliance on small samples hypothesis is not a result of a tendency to rely on the most recent experiences. These findings can be explained by assuming that people rely on those experiences, which seem most similar to the current task, and that the experiences of others seem less similar.

How Rewarding is the Reward? Demand Estimation of Crowdfunding Platforms

Reward-based crowdfunding platforms are best known to facilitate the fund-raising process for entrepreneurs and small businesses. Entrepreneurs seek money from the backers to kick off their projects in exchange for rewards. While the competition among the projects to get more supporters grow, many projects fail to reach their fund-raising target. We use a structural demand estimation to understand the role of different aspects of reward scheme design and pricing. More specifically, we apply an aggregate level discrete choice demand model on Kickstarter projects. We characterize the features of the projects by utilizing the document embedding vectors from natural language processing methods. We treat the endogeneity of the price by applying latent instrument variables and finite mixture model for price. The results show that the price coefficient is biased towards zero in the absence of endogeneity treatment. On the reward scheme design, we show low-level rewards cannibalize the more expensive ones. We also find that low-level rewards have the least contribution in customer welfare. Campaign creators and online platforms benefit from the insights of the model for reward scheme design and pricing.

Technological Review of Permeable Pavement Systems for Applications in Small Island Developing States

This paper presents a technological review of permeable pavement systems (PPS) for applications in urban areas of small island developing states (SIDS). This review includes a literature survey of PPS, challenges and opportunities of climate change and urban development in SIDS, and key aspects worth considering for widespread acceptance and use of PPS in SIDS. SIDS is comprised of several nations located across the Atlantic, Pacific, and Indian Oceans, as well as the Caribbean, Mediterranean, and South China seas. They are particularly susceptible to the effects of climate change and increasing urbanization. Urban stormwater management and flooding are often serious challenges faced by authorities in most SIDS. PPS are resilient infrastructure which can assist in mitigating flooding by reducing runoff and peak flows and improving the landscape perviousness. PPS can further improve stormwater runoff quality to receiving natural waters by reducing pollutant loadings of suspended solids, heavy metals, and hydrocarbons. Unlike most territorial states, the geographical and geologically confined nature of most SIDS present unique parameters for consideration when designing permeable pavements for SIDS. This literature survey found that some of the most important parameters include traffic loads, cost, construction aggregate choice, and availability, permeability of existing soil at the intended location, depth of water table, potential for groundwater contamination, slope of the pavement, stormwater reuse option, clogging, maintenance, and support from policy makers. Continued practical research into the use of permeable pavements for urban runoff management in SIDS is on‐going with the expectation of PPS being utilized in many SIDS.

Adapting neuroeconomics for environmental and energy policy

AbstractNeuroimaging methods provide insight into the neural mechanisms underlying the decision process, characterizing choice at the individual level and, in a growing number of contexts, predicting national- and market-level behavior. This dual capacity to examine heterogeneity while forecasting aggregate choice is particularly beneficial to those studying environmental decision-making. To effectively reduce residential energy usage and foster other pro-environmental behaviors, policy-makers must understand the effects of information frames and behavioral nudges across individuals who hold a diverse array of attitudes toward the environment and face a broad range of barriers to action. This paper articulates the potential of neuroeconomic methods to aid environmental policy-makers interested in behavior change, especially those interested in closing the energy efficiency gap. Investigation into the roles of affect, eco-labeling and social norms will be discussed, as well as personal identity and climate change beliefs. Combining neuroimaging with behavioral economics experiments can inform the development of effective messaging, characterize the influence of individual differences on the decision process and aid in forecasting the efficacy of policy interventions at scale.

A laboratory experiment on the heuristic switching model

We present results from the first laboratory experiment on the seminal heuristic switching model introduced by Brock and Hommes (1997, 1998). Subjects choose between two alternatives, a sophisticated and stabilizing, but costly, heuristic, and a destabilizing, but cheap, heuristic, and are paid according to the performance of the chosen heuristic. Aggregate choices determine the evolution of a state variable and, consequently, the performance of both heuristics. Theoretically, an increase in the costs for the stabilizing heuristic generates instability and leads to endogenous fluctuations in both the state variable and the fraction of agents using that heuristic.We vary the costs of the stabilizing heuristic in the experiment and find that the predictions of the heuristic switching model are partially confirmed. For low costs the dynamics are stable. For high costs, the dynamics initially are unstable and exhibit the type of bubbles and crashes emblematic for the heuristic switching model. However, over time the pattern of bubbles and crashes disappears and the dynamics become more stable. We estimate a standard discrete choice model on aggregate choice data and observe that subjects have a tendency to become less sensitive to payoff differences when the environment is less stable, which has important implications for the application of heuristic switching models.

Estimating panel effects in probabilistic representations of dynamic decision trees using bayesian generalized linear mixture models

When collecting panel data, we need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision trees, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, we elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. Given that panel data naturally have a hierarchical structure with repeated measures nested within individuals, we implement a mixed-effects model that simultaneously accounts for population-level effects (fixed effects), between-individual variances (random effects), and within-individual variances (autocorrelations). The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling (GLMM). When extracting the dynamic probabilistic decision trees, it is assumed that the random effects are known, while it is assumed that the fixed effects are known when estimating the Bayesian GLMM. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. We also test the significance of temporal autocorrelation within individuals. The suggested approach is illustrated using charging station choice of users of Plug-in Electric Vehicles (PEV). Results support the potential value of the suggested approach.

A Dynamic Collective Choice Model with an Advertiser

This paper studies a dynamic collective choice model in the presence of an advertiser, where a large number of consumers are choosing between two alternatives. Their choices are influenced by the group’s aggregate choice and an advertising effect. The latter is produced by an advertiser making investments to convince as many consumers as possible to choose a specific alternative. In schools, for example, teenagers’ decisions to smoke are considerably affected by their peers’ decisions, as well as the ministry of health campaigns against smoking. We model the problem as a Stackelberg dynamic game, where the advertiser makes its investment decision first, and then the consumers choose one of the alternatives. On the methodological side, we use the theory of mean field games to solve the game for a continuum of consumers. This allows us to describe the consumers’ individual and aggregate behaviors, and the advertiser’s optimal investment strategies. When the consumers have sufficiently diverse a priori opinions toward the alternatives, we show that a unique Nash equilibrium exists between them, which predicts the distribution of choices over the alternatives, and the advertiser can always make optimal investments. For a certain uniform distribution of a priori opinions, we give an explicit form of the advertiser’s optimal investment strategy and of the consumers’ optimal choices.

Neuroforecasting Aggregate Choice

Advances in brain-imaging design and analysis have allowed investigators to use neural activity to predict individual choice, while emerging Internet markets have opened up new opportunities for forecasting aggregate choice. Here, we review emerging research that bridges these levels of analysis by attempting to use group neural activity to forecast aggregate choice. A survey of initial findings suggests that components of group neural activity might forecast aggregate choice, in some cases even beyond traditional behavioral measures. In addition to demonstrating the plausibility of neuroforecasting, these findings raise the possibility that not all neural processes that predict individual choice forecast aggregate choice to the same degree. We propose that although integrative choice components may confer more consistency within individuals, affective choice components may generalize more broadly across individuals to forecast aggregate choice.

When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes.

Although traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on a trial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes—even better than choice itself.SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural data has proven elusive; even when successful, neural forecasts have not historically supplanted behavioral forecasts. In the current research, we find that neural responses can forecast market-level choice and outperform behavioral measures in a novel Internet crowdfunding context. Targeted as well as model-free analyses convergently indicated that nucleus accumbens activity can support aggregate forecasts. Beyond providing initial evidence for neuropsychological processes implicated in crowdfunding choices, these findings highlight the ability of neural features to forecast aggregate choice, which could inform applications relevant to business and policy.

Matching with Multilateral Contracts

In many matching environments, agreements are multilateral and/or have externalities. We show that stability in these environments depends on agents' aggregate choice behavior. Two different conditions on aggregate choice ensure a stable outcome exists. One applies when contracts are substitutes; the other ensures a unique stable outcome even without substitutability. Our results apply matching-theoretic techniques to environments where agents' interactions cannot be described by independent bilateral agreements, and also accommodate markets without a two-sided or supply chain structure. Our equivalent characterization of stability also makes computing stable outcomes easier, even in environments outside the scope of our existence results.