Strategic Interaction and Trade Policymaking: Formal Analysis and Simulation

This paper introduces a simple game‐theoretic model and a Monte Carlo simulation of trade negotiations with the aim of identifying the nature of the selection bias that may threaten valid inference from empirical tests relying on data from trade disputes. Insights from the formal model are used to critically engage recent empirical analyses. This model is applied more specifically to the American use of Section 301 as an instrument to prise open foreign markets. The results of the game‐theoretical model and the Monte Carlo simulation demonstrate that, despite significant statistical results, models of trade negotiations might potentially suffer from misspecification due to non‐random selection effects.     

Fuzzy Negotiation Solutions of n-Person Cooperative Games by Knowledge Engineering in Bargaining

A model for negotiation is developed upon the basis of a previous model called Fuzzy Negotiation Solution by Knowledge Engineering. The new model, called Compensatory Negotiation Solution by Knowledge Engineering and FNSKE are based on the combination of knowledge of experts in negotiation, rather than on classical notions of rationality. The experts in negotiation present four propositions in a non-mathematical way and with a natural language, according to the theory of Knowledge Engineering. In the CNSKE model, knowledge is represented through logic predicates, and the calculations are made using the Compensatory Fuzzy Logic system. CFL is a system whose operators satisfy the axioms of Utility. The CFL operators, especially because of the compensation property, are more adequate than the norm and co-norm’s operators to model human decision-making, according to empirical results. The Good Deal Index in CNSKE is statistically estimated from the GDI in FNSKE. This is a quantitative index, which provides the solution concept. The fuzzy function f(C) represents the likelihood the players of a coalition C to reach an agreement into this coalition. Counterpart Convenience Indexes 1, 2 allow each player to select the best coalition for negotiation. The advantage of CNSKE over FNSKE is that the idempotency of conjunction and disjunction operators give every membership function -obtained from the predicates- the possibility to be interpreted by itself. Hence, the truth-values of the CNSKE membership functions can be semantically interpreted. In addition, CNSKE can be easily applied to solve real negotiation problems.     

Effects of food and agricultural imports on domestic factors in the U.S. agricultural sector: a translog cost function framework

The translog cost function of the U.S. agricultural sector unveils dynamic relationships between foreign and domestic inputs. On average, capital and labour are weak substitutes, but they are strong substitutes to food and agricultural imports. Therefore, enhancing trade policies on food and agricultural products should be supplemented by strategic policies aiming at protecting domestic factors' income.     

Negotiation Based on Fuzzy Logic and Knowledge Engineering: Some Case Studies

One of the most recent mathematical models for negotiation is the Compensatory Negotiation Solution by Knowledge Engineering (CNSKE). In this model a logic system called Compensatory Fuzzy Logic was used, which is more adequate to solve problems of decision making than the classical one probabilistic fuzzy logic system. The idempotency axiom of this system and the continuity of the operators allow the truth-values of the membership function to have a cardinal and not exclusively ordinal semantic meaning. On the other hand, continuity also makes ‘sensible’ the truth-values of the predicates. The aim of this paper is to illustrate the advantages of the CNSKE over other approaches in Game Theory. To show these advantages, some case studies are analyzed, consisting on the solution of three problems in which CNSKE is applied in economic and politic cases of negotiation, and compared with other alternative approaches.     

Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals

We propose a new way of selecting among model forms in automated exponential smoothing routines, consequently enhancing their predictive power. The procedure, here addressed as treating, operates by selectively subsetting the ensemble of competing models based on information from their prediction intervals. By the same token, we set forth a pruning strategy to improve the accuracy of both point forecasts and prediction intervals in forecast combination methods. The proposed approaches are respectively applied to automated exponential smoothing routines and Bagging algorithms, to demonstrate their potential. An empirical experiment is conducted on a wide range of series from the M-Competitions. The results attest that the proposed approaches are simple, without requiring much additional computational cost, but capable of substantially improving forecasting accuracy for both point forecasts and prediction intervals, outperforming important benchmarks and recently developed forecast combination methods.     

On the strategic use of risk and undesirable goods in multidimensional screening

A monopolist sells goods possibly with a characteristic consumers dislike (for instance, he sells random goods to risk averse agents), which does not affect the production costs. We investigate the question whether using undesirable goods is profitable to the seller. We prove that in general this may be the case, depending somehow on the correlation between agent types and aversion. This is due to screening effects that outperform this aversion. We analyze, in a continuous framework, several multidimensional cases.