Autism spectrum disorder is defined by the presence of sustained problems in areas of social cognition and social understanding alongside repetitive and/or restricted patterns of behaviour. Behavioural presentations and developmental trajectories in autism are highly heterogeneous. For most, characteristics variably continue across the lifespan, and, for many, they overlap with numerous overrepresented comorbid combinations spanning behavioural, psychiatric and somatic domains. The current autism diagnostic systems (DSM-5, ICD-11) reflect this heterogeneity, focusing on discerning different assistance needs and symptom severity combinations. An emerging view on the pluralisation of autism – ‘the autisms’ – based on different severity levels and different developmental trajectories is gaining popularity, bolstered by the introduction of the grouping ‘profound autism’ and observations of non-persistence of autism for some. We advance the case for expanding the definition of the plural autisms based also on the numerous different aetiological routes that can lead to autism. Various genetic conditions, susceptibility to infectious agents, non-infectious environmental exposures and immune-mediated occurrences have all been observed to culminate in a diagnosis of autism. As a triad, aetiology, presentation intensity and developmental trajectory offer new ways to classify the autisms, with potentially important implications for research and practice.

Experiments on saving behavior reveal substantial heterogeneity of behavior and performance. We show that this heterogeneity is reliable and examine several potential sources of it, including cognitive ability and personality scales. The strongest predictors of both behavior and performance are two cognitive ability measures. We conclude that complete explanations of heterogeneity in dynamic decision making require attention to complexity and individual differences in cognitive constraints.

Norm-based accounts of social behavior in economics typically reflect tradeoffs between maximization of own consumption utility and conformity to social norms. Theories of norm-following tend to assume that there exists a single, stable, commonly known injunctive social norm for a given choice setting and that each person has a stable propensity to follow social norms. We collect panel data on 1468 participants aged 11–15 years in Belfast, Northern Ireland and Bogotá, Colombia in which we measure norms for the dictator game and norm-following propensity twice at 10 weeks apart. We test these basic assumptions and find that norm-following propensity is stable, on average, but reported norms show evidence of change. We find that individual-level variation in reported norms between people and within people across time has interpretable structure using a series of latent transition analyses (LTA) which extend latent class models to a panel setting. The best fitting model includes five latent classes corresponding to five sets of normative beliefs that can be interpreted in terms of what respondents view as “appropriate” (e.g. equality vs. generosity) and how they view deviations (e.g. deontological vs. consequentialist). We also show that a major predictor of changing latent classes over time comes from dissimilarity to others in one’s network. Our application of LTA demonstrates how researchers can engage with heterogeneity in normative perceptions by identifying latent classes of beliefs and deepening understanding of the extent to which norms are shared, stable, and can be predicted to change. Finally, we contribute to the nascent experimental literature on the economic behavior of children and adolescents.

There is ample evidence that people differ considerably in their preferences. We identify individual heterogeneity in type and strength of social preferences in a series of binary three-person dictator games. Based on this identification, we analyze response times in another series of games to investigate the cognitive processes of distributional preferences. We find that response time increases with the number of conflicts between individually relevant motives and decreases with the utility difference between choice options. The selfish motive is more intuitive for subjects who are more selfish. Our findings indicate that the sequential sampling process and the intuition of selfishness jointly produce distribution decisions, and provide an explanation for the mixed results on the correlations between response time and prosociality. Our results also show that it is important to take heterogeneity of preferences into account when investigating the cognitive processes of social decision making.

Competition between groups is ubiquitous in social and economic life, and typically occurs between groups that are not created equal. Here we experimentally investigate the implications of this general observation on the unfolding of symmetric and asymmetric competition between groups that are either homogeneous or heterogeneous in the ability of their members to contribute to the success of the group. Our main finding is that relative to the benchmark case in which two homogeneous compete against each other, heterogeneity within groups per se has no discernable effect on competition, while introducing heterogeneity between groups leads to a significant intensification of conflict as well as increased volatility, thereby reducing earnings of contest participants and increasing inequality. We further find that heterogeneous groups share the labor much more equally than predicted by theory, and that in asymmetric contests group members change the way in which they condition their efforts on those of their peers. Implications for contest designers are discussed.

Voluntary carbon markets present firms and individuals with the opportunity to offset all or part of their carbon footprints. We report on a controlled laboratory experiment to understand the behavioral motivations driving the purchase of carbon offsets, in addition to investigating the effect of the introduction of voluntary carbon markets on emission-causing activities. We find a stable demand for offsets when the price is sufficiently low. Behavior is, however, heterogeneous. Individuals with a high (low) personal-responsibility index increase their offset purchases as their own damage (total damages) increases, but do not condition their offsetting behavior on the total damages (own damage) generated. We also show that, when individuals trade in competitive markets, the availability of offsets does not affect the total damages generated. Introduction of carbon offsets increases individuals’ earnings by eliminating some of the damages ex-post, but does not increase economic efficiency.

We study learning and selection and their implications for possible effort escalation in a simple game of dynamic property rights conflict: a multi-stage contest with random resolve. Accounting for the empirically well-documented heterogeneity of behavioral motives of players in such games turns the interaction into a dynamic game of incomplete information. In contrast to the standard benchmark with complete information, the perfect Bayesian equilibrium features social projection and type-dependent escalation of efforts caused by learning. A corresponding experimental setup provides evidence for type heterogeneity, for belief formation and updating, for self-selection and for escalation of efforts in later stages.

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide an individually-measurable correlate of cognitive effort. We test a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games.

Psychological games of guilt aversion assume that preferences depend on (beliefs about) beliefs and on the guilt sensitivity of the decision-maker. We present an experiment designed to measure guilt sensitivities at the individual level for various stake sizes. We use the data to estimate a structural choice model that allows for heterogeneity, and permits that guilt sensitivities depend on stake size. We find substantial heterogeneity of guilt sensitivities in our population, with 60% of decision makers displaying stake-dependent guilt sensitivity. For these decision makers, we find that average guilt sensitivities are significantly different from zero for all stakes considered, while significantly decreasing with the level of stakes.

Previous research demonstrates that individuals vary in their social preferences. Less well-understood is how group composition affects the behavior of different social preference types. Does one bad apple really spoil the bunch? This paper exogenously identifies experimental participants’ social preferences, then systematically assigns individuals to homogeneous or heterogeneous groups to examine the impact of ‘bad apples’ on cooperation and efficiency. Consistent with previous research, we find that groups with more selfish types achieve lower levels of efficiency. We identify two mechanisms for the effect. First, the selfish players contribute less. Second, selfish players induce lower contributions from the conditional cooperators, and this effect increases in the number of selfish players. These results are not sensitive to information about the distribution of types in the group.

State-owned enterprises (SOEs) in China play a critical role in national economic development and the country's positioning on the global stage. Chinese SOEs have undergone substantial transformations from traditional government-run entities to a variety of corporate forms exhibiting different levels of state involvement. Despite their substantial influence, the internal diversity of SOEs – from wholly state-owned to mixed-ownership – has not been thoroughly examined. This paper provides an overview of SOEs' critical roles in the Chinese economy, the relationship between SOEs and privately owned enterprises (POEs), and the challenges of SOEs in different stages of Chinese economic development. It then introduces five research papers that explore the institutional, strategic, and organizational perspectives on how SOEs manage the dual pressures of state and market logic, respond to policy adjustments, tackle leadership challenges, and navigate current global trends such as digital transformation, technological innovation, and environmental sustainability. In this paper, we provide important implications for policy and managerial practices and highlight a future research agenda for the heterogeneity of Chinese SOEs, and how SOEs respond to these challenges in the evolving geopolitical landscape, adapt their strategies, and manage relationships with foreign governments and enterprises under such conditions.

The Republic of Sakha (Yakutia) faces serious demographic challenges. One of the most important among them is the imbalance of population flows within internal migration. This paper examines the patterns of internal migration in the Republic, based on the distribution of municipal districts (uluses) by economic zones designated by the authorities for administrative purposes. The six most common indices characterising the intensity of migration of the population were used for the analysis. The homogeneity of Yakutia’s districts according to these indices was tested using the van der Waerden test. The article reveals that the intensity of migration in Yakutia has increased since 2011. The financial crisis of 2008–2009 and the COVID-19 pandemic had a significant but temporary impact on internal migration in Yakutia. Only Yakutsk has experienced population growth due to internal migration throughout the period studied. The intensity of migration in the Arctic uluses was not statistically different from central and eastern uluses, but differed from the most economically developed districts in southern and western Yakutia. The Republic was homogeneous with respect to the balance of migration inflows and outflows, but there was considerable heterogeneity in terms of the impact of migration on the size of the population.

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.

In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions.

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to model overall price sensitivity (i.e., indicated by higher-order factor scores) as a function of household-level covariates. All model parameters are estimated simultaneously to circumvent the downward bias resulting from two-stage estimation. The modeling framework is illustrated using scanner panel data from multiple categories of instant coffee.

This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other population parameters fixed, in the marginal space (based on marginal probabilities) and in the full parameter space (based on cell probabilities). The case of a 2 × 2 contingency table is discussed in detail, and a generalization to 2J tables with J ≥ 3 is sketched. Our approach highlights the main distinction between the GoM model and the probabilistic mixture of classes by demonstrating geometrically the difference between the concepts of partial and probabilistic memberships. By using the geometric approach we show that, in special cases, the GoM model can be thought of as being similar to an item response theory (IRT) model in representing population heterogeneity. Finally, we show that the GoM item parameters can provide quantities analogous to more general logistic IRT item parameters. As a latent structure model, the GoM model might be considered a useful alternative for a data analysis when both classes of extreme responses, and additional heterogeneity that cannot be captured by those latent classes, are expected in the population.

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic example and a consumer psychology study involving categories of restaurant brands illustrate how the application of the proposed methodology to the new sorting task can account for a variety of categorization phenomena including multiple category memberships and for heterogeneity through individual differences in the saliency of latent category structures.

The p-median offers an alternative to centroid-based clustering algorithms for identifying unobserved categories. However, existing p-median formulations typically require data aggregation into a single proximity matrix, resulting in masked respondent heterogeneity. A proposed three-way formulation of the p-median problem explicitly considers heterogeneity by identifying groups of individual respondents that perceive similar category structures. Three proposed heuristics for the heterogeneous p-median (HPM) are developed and then illustrated in a consumer psychology context using a sample of undergraduate students who performed a sorting task of major U.S. retailers, as well as a through Monte Carlo analysis.

Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression model that enables the estimation of individual-level-regression coefficients in cross-sectional data involving a single observation per response unit. A Gibbs sampling algorithm is developed to implement the proposed Bayesian methodology. A Monte Carlo simulation study is constructed to assess the performance of the proposed methodology across a number of experimental factors. We then apply the proposed method to analyze data collected from a consumer psychology study that examines the differential importance of price and quality in determining perceived value evaluations.