RATIONAL HEURISTICS? EXPECTATIONS AND BEHAVIORS IN EVOLVING ECONOMIES WITH HETEROGENEOUS INTERACTING AGENTS

We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information, coordination hurdles, and structural breaks. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naïve learning rules. Our results suggest that fast and frugal robust heuristics may not be a second-best option but rather “rational” responses in complex and changing macroeconomic environments. (JEL C63, D8, E32, E6, O4)     

Expectations of Career Counseling and Their Effect on Client Satisfaction

Although there has been growing attention to clients' expectations of career counseling, more research is necessary to clarify the role of these expectations in shaping client satisfaction at the end of the intervention. On the basis of expectation confirmation theory, this study examined the indirect effect of clients' initial expectations on overall satisfaction through the perceived performance and final confirmation of expectations. We also explored whether this indirect effect is dependent on counselors' initial expectations. Longitudinal data were collected on 83 counselor-client dyads involved in a career counseling intervention in northeast Italy. Results confirmed that clients' initial expectations predicted overall satisfaction with the intervention through the subsequent mediation of perceived performance and confirmation of clients' expectations. The moderating role of the counselors' initial expectations was not confirmed. These findings imply that career counselors should invest their efforts in managing clients' expectations.     

Factors Constraining Building Effective and Fair Geographical Indications for Coffee: Insights from a Dominican Case Study

The commodity nature of green coffee is the main cause of the ‘coffee paradox’ (falling producer prices and rising consumer prices). Geographical Indications (GIs) may be an effective ‘decommodifier’ of the coffee market, but many constraints have to be overcome. Based on an analysis of how the characteristics of the coffee production system shape the positions of local stakeholders and, thus, the GI building process, this article identifies some constraining factors that can seriously undermine the capacity the GI has to decommodify the market and attain a fairer distribution of the benefits for local producers.     

Bringing Cognition Back In and Moving Forward

Journal of Management and Governance -     

A fuzzy logic approach to poverty analysis based on the Gini and Bonferroni inequality indices

In the poverty analysis framework, a great deal of attention has been paid to the poverty measurement in terms of monetary variables, such as income or consumption. In this context, a relevant open problem is connected with the distinction between poor and non-poor. In fact, the concept of poverty is rather vague and cannot be defined in a clear way. In this respect, following a fuzzy logic approach, some new poverty measures are proposed. In particular, the fuzzy extension of two existing poverty measures based on the Gini and Bonferroni inequality indices is provided. Some synthetic and real applications are given in order to show how the proposed poverty measures work.     

Genetic algorithms for the analysis of Bayesian hierarchical partition models

Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.     

Perceived Corruption and Individuals’ Life Satisfaction: The Mediating Role of Institutional Trust

Social Indicators Research - Corruption degrades the quality of institutions, increases economic inequality and limits growth. Recent studies indicate that corruption is also associated with lower...     

Whither the evolution of the contemporary social fabric? New technologies and old socio‐economic trends

This article considers whether societies are witnessing another industrial revolution in the light of an assessment of the impact of technological change on today's socio‐economic fabric, especially with respect to employment, income distribution, working conditions and labour relations. The authors argue that the processes of innovation and the spread of what they term “intelligent automation” are likely to exacerbate incumbent patterns of uneven income distribution and power, some of which existed well before the arrival of the technologies concerned, while others have emerged over the past 30 to 40 years. They venture to consider policy implications on the basis of such developments.     

A Bayesian model for cross-study differential gene expression

In this paper we define a hierarchical Bayesian model for microarray expression data collected from several studies and use it to identify genes that show differential expression between two conditions. Key features include shrinkage across both genes and studies, and flexible modeling that allows for interactions between platforms and the estimated effect, as well as concordant and discordant differential expression across studies. We evaluated the performance of our model in a comprehensive fashion, using both artificial data, and a "split-study" validation approach that provides an agnostic assessment of the model's behavior not only under the null hypothesis, but also under a realistic alternative. The simulation results from the artificial data demonstrate the advantages of the Bayesian model. The 1 - AUC values for the Bayesian model are roughly half of the corresponding values for a direct combination of t- and SAM-statistics. Furthermore, the simulations provide guidelines for when the Bayesian model is most likely to be useful. Most noticeably, in small studies the Bayesian model generally outperforms other methods when evaluated by AUC, FDR, and MDR across a range of simulation parameters, and this difference diminishes for larger sample sizes in the individual studies. The split-study validation illustrates appropriate shrinkage of the Bayesian model in the absence of platform-, sample-, and annotation-differences that otherwise complicate experimental data analyses. Finally, we fit our model to four breast cancer studies employing different technologies (cDNA and Affymetrix) to estimate differential expression in estrogen receptor positive tumors versus negative ones. Software and data for reproducing our analysis are publicly available.