Foreign direct investment: A genetic algorithm approach

This paper proposes a genetic algorithm (GA) approach as an analytical tool with a carefully defined fitness function for variable selection. Discriminant analysis will be used as a parameter evaluation method for the analysis of inward foreign direct investment (FDI) in China. Results indicate that the proposed GA method is more efficient in classifying “successful or unsuccessful” inward FDI by providing higher accuracy rates while using fewer variables than previous efforts. An implication of this result is that, given a scarcity of resources and the need to promote FDI, the proposed GA may provide a more efficient way to concentrate on those fewer variables found to be important determinants of “successful” FDI inflow.     

A genetic algorithm based approach to transactional and QoS-aware service selection

As there are various risks of failure when Web services are deployed in unreliable environment, the execution of a composite Web service (CWS) requires the transaction mechanism to guarantee its reliable execution. However, the existing service selection approaches consider QoS and transaction separately and have not considered that transactional properties may affect the QoS such as the execution time of a CWS. This work addresses the importance of considering transactional properties and how the transactional properties affect the QoS simultaneously in the process of service selection by a QoS-aware and transactional-aware selection approach. First, a performance evaluation method is proposed to calculate the execution time of a transactional CWS. Then, a genetic algorithm based approach, which takes into account the execution time, price, transactional property and successful execution rate of CWS, is presented to achieve global optimisation service selection. Finally, experimental results show the efficiency and effectiveness of the approach.     

Efficient learning via simulation: A marginalized resample-move approach

In state–space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out the states using a particle filter. Second, it applies a Bayesian resample-move approach to this marginalized system. The methodology is generic and needs little design effort. Different from batch estimation methods, it provides posterior quantities necessary for full sequential inference and recursive model monitoring. The algorithm is implemented both on simulated data in a linear Gaussian model for illustration and comparison and on real data in a Lévy jump stochastic volatility model and a structural credit risk model.     

A genetic algorithm for the partial binary constraint satisfaction problem: an application to a frequency assignment problem

We describe a genetic algorithm for the partial constraint satisfaction problem. The typical elements of a genetic algorithm, selection, mutation and cross-over, are filled in with combinatorial ideas. For instance, cross-over of two solutions is performed by taking the one or two domain elements in the solutions of each of the variables as the complete domain of the variable. Then a branch-and-bound method is used for solving this small instance. When tested on a class of frequency assignment problems this genetic algorithm produced the best known solutions for all test problems. This feeds the idea that combinatorial ideas may well be useful in genetic algorithms.     

Buying cooperation in an asymmetric environmental differential game

We consider a two-player asymmetric differential game of pollution control. One player is non-vulnerable to pollution, or unwilling to consider damages when choosing her production policy in a non-cooperative game. We characterize the feedback-Nash equilibrium and the cooperative solution. We establish conditions under which the vulnerable player can buy the cooperation of the non-vulnerable player to control her emissions. We further use the Nash bargaining solution to allocate the total cooperative dividend between the two players and propose a time-consistent decomposition overtime of the total payoff.     

Working and organizing in the age of the learning algorithm

Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as the extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential.     

Forecasting bank failures and stress testing: A machine learning approach

This paper presents a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary that separates the solvent banks from those that failed. This setup generates a novel alternative stress-testing tool. Our sample of 1443 U.S. banks includes all 481 banks that failed during the period 2007–2013. The set of explanatory variables is selected using a two-step feature selection procedure. The selected variables were then fed to a support vector machines forecasting model, through a training–testing learning process. The model exhibits a 99.22% overall forecasting accuracy and outperforms the well-established Ohlson’s score.     

Popularity of reinforcement-based and belief-based learning models: An evolutionary approach

In an evolutionary model, players from a given population meet randomly in pairs each instant to play a coordination game. At each instant, the learning model used is determined via some replicator dynamics that respects payoff fitness. We allow for two such models: a belief-based best-response model that uses a costly predictor, and a costless reinforcement-based one. This generates dynamics over the choice of learning models and the consequent choices of endogenous variables. We report conditions under which the long run outcomes are efficient (or inefficient) and they support the exclusive use of either of the models (or their co-existence).     

An improved genetic algorithm for robust design in multivariate systems

In a previous article, we presented a genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems. Based on that GA, we developed a new GA that uses a new desirability function, based on the aggregation of the observed variance of the responses and the squared deviation between the mean of each response and its corresponding target value. Additionally, we also changed the crossover operator from a one-point to a uniform one. We used three different case studies to evaluate the performance of the new GA and also to compare it with the original one. The first case study involved using data from a univariate real system, and the other two employed data obtained from multivariate process simulators. In each of the case studies, the new GA delivered good solutions, which simultaneously adjusted the mean of each response to its corresponding target value. This performance was similar to the one of the original GA. Regarding variability reduction, the new GA worked much better than the original one. In all the case studies, the new GA delivered solutions that simultaneously decreased the standard deviation of each response to almost the minimum possible value. Thus, we conclude that the new GA performs better than the original one, especially regarding variance reduction, which was the main problem exhibited by the original GA.     

On learning and the economics of firm efficiency: a state-contingent approach

The paper investigates the microeconomics of learning for an owner-managed firm. The analysis relies on a general state-contingent approach to uncertainty. In the presence of price and production uncertainty, it examines the case where acquiring information is costly. It evaluates the microeconomic efficiency of decisions, with a focus on three factors: sure income, the value of information, and a risk premium (measuring the implicit cost of private risk bearing). The analysis stresses the role of information and learning in microeconomic decisions. Implications for efficiency and productivity measurements are discussed.     

Fundamental ratios as predictors of ESG scores: a machine learning approach

Decisions in Economics and Finance - Sustainable and responsible finance incorporates Environmental, Social, and Governance (ESG) principles into business decisions and investment strategies. In...     

Internalized social norms in conflicts: an evolutionary approach

An individual with an internalized norm prefers to comply with the norm as long as the opponents also comply. This paper studies the evolution of such norms in a conflict. Individuals play with the same opponent for a number of rounds, during which they adapt their behavior according to a myopic best response dynamic. The payoffs earned during this equilibration period enter into the cultural evolutionary process that determines which norms that survive. It is found that if agents play against the same opponent for sufficiently many times, then only norms that reduce conflict relative to the Nash equilibrium survive. If instead agents meet the same opponent only once, then only agents that play the Nash equilibrium survive. By way of example it is also shown that in the intermediate case the outcome of evolution can be either that conflict reducing norms or Nash behavior dominates. These results indicate that the common practice of disregarding equilibration in models of preference evolution is not without a cost.     

Rent-seeking in an emerging market: A DSGE approach

Due to the fact that rent-seeking is by definition an unobservable variable, measuring its size and evolution over the business cycle can be a daunting challenge. In this article, by embedding rent-seeking behavior in an otherwise standard open-economy DSGE model, we are able to derive a quarterly time series of this variable (expressed as a percentage deviation from the trend) for an emerging economy such as Brazil. The estimated series, spanning the period 2002Q1?2017Q4, shows a strong positive correlation with the “Commodity Super Cycle” of the 2000 decade and falls as a result of some political scandals and their ensuing investigations, among other driving forces. We also rely on the same model to assess how several shocks hitting the economy affect both rent-seeking and the relevant macroeconomic variables in our model. Barring monetary expansions, increased exports and higher income transfers to households, expansionary shocks are associated with lower rent-seeking activity. Factoring in these two sets of results, the upshot is that rent-seeking behavior shows a pattern of procyclicality in the Brazilian economy.     

Dynamic bank runs: an agent-based approach

Journal of Economic Interaction and Coordination - This paper simulates bank runs by using an agent-based approach to assess the depositors’ behavior under various scenarios in a...     

Productivity and unemployment: an ABM approach

Journal of Economic Interaction and Coordination - We investigate the relationship between productivity and unemployment with an ABM approach. In particular, we use the framework of Riccetti et al....     

Common holdings and strategic manager compensation: The case of an asymmetric triopoly

We study an asymmetric triopoly in a heterogeneous product market where quantity decisions are delegated to managers. The two biggest firms are commonly owned by shareholders such as index funds, whereas the smallest firm is owned by independent shareholders. Under such a common holding owner structure, the owners have an incentive to coordinate when designing their manager compensation schemes. This coordination leads to a reallocation of production and induces a redistribution of profits. The trade volume in the market is reduced so that shareholder coordination is detrimental to consumer surplus as well as welfare.     

Demand chain management: an integrative approach in automotive retailing

Recent reviews of the ‘operations management’ (OM) research literature have shown a surprising little shift towards empirical methodologies from traditional modelling solution methods. In addition, there is generally a lack of empirical testing and validation of manufacturing strategies. Coupled to this fact is that empirical work is rarely carried out in an inter-disciplinary manner, with the complete combination of OM, marketing, strategy, lean thinking and accounting approaches particularly poorly researched. Fully cross-functional integrative empirical research is required to help support the understanding of the applicability of OM practices within industry. This paper explores such an integration of approaches developed within the ‘lean thinking, strategic cost management, marketing and policy deployment areas’. In order to investigate the approach a single automotive retailer is used as an instrumental case. The results of the study show that the new approach stands up well as an integrative approach that can prove highly beneficial results. However, the pilot work also identified a number of limitations that are the basis of further refinement and testing of the method.     

Optimal taxation in an RBC model: A linear-quadratic approach

We reconsider the optimal taxation of income from labor and capital in the stochastic growth model analyzed by Chari et al. [1994. Optimal fiscal policy in a business cycle model. Journal of Political Economy 102, 617–652; 1995. Policy analysis in business cycle models. In: Cooley, T.F. (Ed.), Frontiers of Business Cycle Research. Princeton University Press, Princeton], but using a linear-quadratic (LQ) approximation to derive a log-linear approximation to the optimal policy rules. The example illustrates how inaccurate ‘naive’ LQ approximation – in which the quadratic objective is obtained from a simple Taylor expansion of the utility function of the representative household – can be, but also shows how a correct LQ approximation can be obtained, which will provide a correct local approximation to the optimal policy rules in the case of small enough shocks. We also consider the numerical accuracy of the LQ approximation in the case of shocks of the size assumed in the calibration of Chari et al. We find that the correct LQ approximation yields results that are quite accurate, and similar in most respects to the results obtained by Chari et al. using a more computationally intensive numerical method.     

Determinants of knowledge-based entrepreneurship: an exploratory approach

We intend to further the research related to the factors that determine successful entrepreneurial endeavours by academic researchers, and thus put forth an exploratory model of Knowledge-Based Entrepreneurship (KBE). Given the lack of extant adequate indicators, a new scale was developed based on the most frequently cited constructs in the literature. A sample of 1,401 researchers from Portuguese universities was administered a questionnaire and the data collected permitted a validation with regards to the psychometric properties of the questionnaire, to see its applicability to the study. The findings suggest that the absence of a patenting history or start-up portfolio act as significant barriers to academic entrepreneurship. However, when the institutional strategy is to increase patenting and spin-off activities, the university should begin investing in creating a networking environment capable of reinforcing the researchers’ Social Capital. Observing the example of successful entrepreneurs motivates other researchers to consider the possibility of developing their own ventures. The Structural Equations Modelling (SEM) approach allowed for us to identify and measure the non-linear relationships that shape the core of KBE and influence the attainment of measurable outcomes aimed at encouraging entrepreneurship. A relatively high share of the variance of the dependent variables is explained by the model, ensuring their representativeness and contributing to the state-of-art of the knowledge in this research field.