Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quan...Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.展开更多
This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a prev...This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.展开更多
The past decade witnessed rapid development of constraint satisfaction technologies, where algorithms are now able to cope with larger and harder problems. However, owing to the fact that constraints are inherently de...The past decade witnessed rapid development of constraint satisfaction technologies, where algorithms are now able to cope with larger and harder problems. However, owing to the fact that constraints are inherently declarative, attention is quickly turning toward developing high-level programming languages within which such problems can be modeled and also solved. Along these lines, this paper presents DEPICT, the language. Its use is illustrated through modeling a number of benchmark examples. The paper continues with a description of a prototype system within which such models may be interpreted. The paper concludes with a description of a sample run of this interpreter showing how a problem modeled as such is typically solved.展开更多
文摘Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.
文摘This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.
基金This work was supported by Lebanese National Council for Scientific Research.
文摘The past decade witnessed rapid development of constraint satisfaction technologies, where algorithms are now able to cope with larger and harder problems. However, owing to the fact that constraints are inherently declarative, attention is quickly turning toward developing high-level programming languages within which such problems can be modeled and also solved. Along these lines, this paper presents DEPICT, the language. Its use is illustrated through modeling a number of benchmark examples. The paper continues with a description of a prototype system within which such models may be interpreted. The paper concludes with a description of a sample run of this interpreter showing how a problem modeled as such is typically solved.
