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. Mainstream economists currently base their theory of expectation formation on the assumption of rationality Rationality implies … unbiased forecasts and learning from past mistakes. Psychologists, however, see people as guided by processes other than the … assumption of rationality. These processes often result in biased predictions and a failure to learn from past mistakes. This …
Persistent link: https://www.econbiz.de/10005641800
Microeconomics' notions of “market supply†and “market demand†do not exist in real-world markets. Its models give a central place to equilibria, implying that they are predictions. It distracts from more essential aspects of economic behavior and exchange and encourages...
Persistent link: https://www.econbiz.de/10010797367
Are financial markets predictable? How to predict the financial markets? These important questions are not answerable in the existing framework of either finance or economics. This paper shows in details that these questions are also not answerable in the existing framework of modern physics. In...
Persistent link: https://www.econbiz.de/10011113488
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Equilibrium notions for games with unawareness in the literature cannot be interpreted as steady-states of a learning … that possesses a self-confirming equilibrium in extensive-form rationalizable strategies. This notion of equilibrium can be … interpreted as steady-state of both a discovery and learning process. …
Persistent link: https://www.econbiz.de/10012509154
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are doomed to "overfit" the data and therefore engage in ineffective learning. The analysis places no computational or …
Persistent link: https://www.econbiz.de/10011599460
The use of coarse categories is prevalent in various situations and has been linked to biased economic outcomes, ranging from discrimination against minorities to empirical anomalies in financial markets. In this paper we study economic rationales for categorizing coarsely. We think of the way...
Persistent link: https://www.econbiz.de/10011380993
A novel neural architecture for prediction in industrial control: the 'Double Recurrent Radial Basis Function network … network on the prediction values confirmed that the proposed architecture minimizes the prediction error. The proposed R2RBF … input and output layers. Given the learning complexity of neural networks with the use of the back-propagation training …
Persistent link: https://www.econbiz.de/10010569693