Using the Myers-Briggs Type Indicator (MBTI) for Modeling Multiagent Systems

The formation of high-performance teams has been a constant challenge for organizations, which despite considering human capital as one of the most important resources, it still lacks the means to allow them to have a better understanding of several factors that influence the formation of these teams. In this sense, studies also demonstrate that teamwork has a significant impact on the results presented by organizations, in which human behavior is highlighted as one of the main aspects to be considered in the building of work teams. The Myers-Briggs Type Indicator seeks to classify the behavioral preferences of individuals around eight characteristics, which grouped as dichotomies, describe different psychological types. With it, researchers have sought to expand the ability to understand the human factor, using strategies with multiagent systems that, through experiments and simulations, using computer resources, enable the development of artificial agents that simulate human actions. In this work, we present an overview of the research approaches that use MBTI to model agents, aiming at providing a better knowledge of human behavior. Additionally, we make a preliminary discussion of how these results could be explored in order to advance the studies of psychological factors' influence in organizations' work teams formation.

Mosquito Olfactory Response Ensemble: a curated database of behavioral and electrophysiological responses enables pattern discovery

Many experimental studies have examined behavioral and electrophysiological responses of mosquitoes to odors. However, the differences across studies in data collection, processing, and reporting make it difficult to perform large-scale analyses combining data from multiple studies. Here we extract and standardize data for 12 mosquito species, along with Drosophila melanogaster for comparison, from over 170 studies and curate the Mosquito Olfactory Response Ensemble (MORE), publicly available at https://neuralsystems.github.io/MORE. We demonstrate the ability of MORE in generating biological insights by finding patterns across studies. Our analyses reveal that ORs are tuned to specific ranges of several physicochemical properties of odorants; the empty-neuron recording technique for measuring OR responses is more sensitive than the Xenopus oocyte technique; there are systematic differences in the behavioral preferences reported by different types of assays; and odorants tend to become less attractive or more aversive at higher concentrations.

Dynamics of Mask Use as a Prevention Strategy against SARS-CoV-2 in Panama

Early in the SARS-CoV-2 pandemic, many national public health authorities implemented non-pharmaceutical interventions to mitigate disease outbreaks. Panamá established mandatory mask use two months after its first documented case. Initial compliance was high, but diverse masks were used in public areas. We studied behavioral dynamics of mask use through the first two COVID-19 waves in Panama, to improve the implementation of effective, low-cost public health containment measures when populations are exposed to novel air-borne pathogens. Mask use behavior was recorded from pedestrians in four Panamanian populations (August to December 2020). We recorded facial coverings and if used, the type of mask, and gender and estimated age of the wearer. Our results showed that people were highly compliant (>95%) with mask mandates and demonstrated important population-level behaviors: (1) decreasing use of cloth masks over time, and increasing use of surgical masks; (2) mask use was 3-fold lower in suburban neighborhoods than other public areas and (3) young people were least likely to wear masks. Results help focus on highly effective, low-cost, public health interventions for managing and controlling a pandemic. Considerations of behavioral preferences for different masks, relative to pricing and availability, are essential for optimizing public health policies. Policies to increase the availability of effective masks, and behavioral nudges to increase acceptance, and to facilitate mask usage, during the ongoing SARS-CoV-2 pandemic, and for future pandemics of respiratory pathogens, are key tools, especially for nations lagging in access to expensive vaccines and pharmacological approaches.

Role of Education and Mentorship in Entrepreneurial Behavior: Mediating Role of Self-Efficacy

Farmers have been very precious for societies for ages. Their active experiments, valuable knowledge about their surroundings, environment, and crops’ requirements have been a vital part of society. However, the psychological perspectives have been a hole in the loop of farming. Hence, this study has investigated the antecedents of entrepreneurial behaviors of farmers with the mediating risk of their entrepreneurial self-efficacy (ESE). The population chosen for this study was the farming community of suburbs of China, and a sample size of 300 was selected for the data collection. This is a survey study, where a structured questionnaire was adapted on a five-point Likert scale. The data were collected from the farming community to know their psychological and behavioral preferences about their profession. This study has produced interesting results that education, training, and intrinsic motivation play a vital role in farmers’ ESE, affecting their entrepreneurial behaviors. This study will add to the body of knowledge and provide an eminent path for emerging entrepreneurs to find more mentorship opportunities to overcome the limitations in upcoming endeavors influencing education and training.

Quantifying the Preference of Stakeholders in the Utilization of Forest Resources

The economic contribution of forest resources to the communities surrounding nature reserves cannot be ignored. The method for which to find a forest resource utilization path to balance the contradiction between local farmers’ economic development and ecological protection in the development of nature reserves is important. However, little attention has been given to the effect of forest resource users’ behavioral preferences on forest resource utilization. This study selected Wolong Nature Reserve as a case study and randomly interviewed different stakeholders with semi-structured questionnaires to investigate the differences in forest resource utilization patterns among stakeholders with different behavioral preferences. According to the results of multi-attribute decision analysis with behavioral preference, stakeholders form different behavioral preferences by judging their own resource endowment. With a change of in the behavioral preference value λ, when the behavioral preference of stakeholders is more pessimistic (λ = 0.1), cautious (λ = 0.3), or neutral (λ = 0.5), they are more inclined to choose the economically dominant forest resource utilization mode; when the behavioral preference of stakeholders is optimistic (λ = 0.7) or even radical (λ = 0.9), they choose the eco-economic or eco-dominant forest resource utilization mode, respectively. This study confirms that stakeholders’ behavioral preferences have an important impact on forest resource utilization patterns. Therefore, policy making should focus on improving the economic benefits of forest resources and providing alternative livelihoods, which will change the resource endowment of the stakeholders of nature reserve, guide them to turn to relatively optimistic behavioral preferences, enhance their awareness and motivation of ecological protection, and thereby improve forest conservation outcomes.

Neuromodulation and Individuality

Within populations, individuals show a variety of behavioral preferences, even in the absence of genetic or environmental variability. Neuromodulators affect these idiosyncratic preferences in a wide range of systems, however, the mechanism(s) by which they do so is unclear. I review the evidence supporting three broad mechanisms by which neuromodulators might affect variability in idiosyncratic behavioral preference: by being a source of variability directly upstream of behavior, by affecting the behavioral output of a circuit in a way that masks or accentuates underlying variability in that circuit, and by driving plasticity in circuits leading to either homeostatic convergence toward a given behavior or divergence from a developmental setpoint. I find evidence for each of these mechanisms and propose future directions to further understand the complex interplay between individual variability and neuromodulators.

Supply Chain Contract Choice with Different Behavior Preferences of Retailer

Retailer may exhibit irrationality when facing the risk of demand uncertainty; therefore, we consider four retailer behavioral preferences: risk neutral (RN), waste aversion (WA), stockout aversion (SA), and stockout-waste aversion (SW). The decision-making and contract choice of upstream and downstream enterprises in cases where demand depends on supplier’s effort are studied. The results show that if the retailer has only SA or RN preferences, then the supplier prefers to choose a wholesale price contract, while the retailer does the opposite, if the retailer has only WA, then the supplier prefers to choose a revenue sharing contract, but the retailer’s contract choice depends on the degree of waste aversion, and if the retailer has SW, then the contract choice of upstream and downstream enterprises is related to the degree of waste aversion and stockout aversion.

The Incentive Game Under Target Effects in Ridesharing: A Structural Econometric Analysis

Problem definition: We study a ridesharing platform’s optimal bonus-setting decisions for capacity and profit maximization problems in which drivers set daily income targets. Academic and Practical Relevance: Sharing-economy companies have been providing monetary rewards to incentivize self-scheduled drivers to work longer. We study the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. Methodology: We model a driver’s decision-making processes and the platform’s optimization problem as a Stackelberg game. Then, utilizing comprehensive datasets obtained from a leading ridesharing platform, we develop a novel empirical strategy to provide evidence on the existence of drivers’ income-targeting behavior through a reduced-form and structural analysis. Furthermore, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios by using the characteristics of heterogeneous drivers derived from the estimation outcomes. Results: Our theoretical model suggests that the drivers’ working hours do not increase monotonically with the bonus rate under the target effect and that the platform may not use all its budget on bonuses to maximize capacity or profit. We empirically demonstrate that the drivers engage in income-targeting behavior, and furthermore, we estimate the income targets for heterogeneous drivers. Through counterfactual analysis, we illustrate how the optimal bonus scheme varies when the platform faces different driver compositions and market conditions. We also find that, compared with the platform’s previous bonus setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 26%, boosting the total profit by $4.3 million per month. Managerial implications: It is challenging to develop a flexible self-scheduled supply of drivers that can match the ever-changing demand and maintain the market share of the ridesharing platform. When offering monetary bonuses to incentivize drivers to work longer, the drivers’ income-targeting behavior can undermine the effectiveness of such bonus schemes. The platform needs to understand the heterogeneity of drivers’ behavioral preferences regarding monetary rewards to design an effective bonus strategy.

A rational delegating computation protocol based on reputation and smart contract

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Next, to satisfy the fairness of the rational delegating computation, we propose a rational delegating computation protocol based on reputation and smart contract. More specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. The reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Then, we conduct a comprehensive experiment to evaluate the proposed protocol. The simulation and analysis results show that the proposed protocol solves the complex traditional verification problem. We also conduct a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network. Meanwhile, we prove the fairness and correctness of the protocol.

Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data

Due to a growing appreciation for the ecological and recreational benefits of public green spaces, the evaluation of urban parks’ service efficiency, as well as citizens’ behavioral preferences for daily recreation, have become an increasing academic focus. However, due to the lack of empirical approaches, existing research on exploring park service areas has been simplified by their use of Euclidean distance or buffer sets by simulation, ignoring the fact that the likelihood of citizens visiting urban parks is time sensitive. Utilizing mobile signaling data and web map services, this study proposes an approach to estimating the travel times of park visitors and analyzing the characteristics of park service areas from the perspective of actual time consumption. Taking Shanghai as a case study, this research firstly identified the time–cost decay of parks with different areas and locations. A comparison analysis was then used to examine the spatial relationship between park service areas and their accessibility defined by time consumption. The results show that (1) urban parks in Shanghai have larger mean service radii than existing planning guidelines, and park service areas were significantly influenced by park locations; (2) people have a great preference for urban parks whose travel times by public transit are under 40 min, and they have no desire to visit parks located within or outside the Middle Ring Road when the travel times reach 60 min and 75 min, respectively; (3) the shapes of park service areas are consistent with the high-accessibility districts defined by time thresholds, in spite of some differences caused by citizens’ choices. These findings provide an effective tool for evaluating the actual characteristics of park recreational services, along with direct implications for policymakers aiming to establish effective strategies for improving the accessibility and vitality of urban parks.