Today I (Meditya) presented a Herbert A. Simon's Paper entitled "Machine as Mind" appeared in "Android Epistemology" by C.Glymour, K.Ford, and P.Hayes.
Defining that the primitives of mind consist of, Symbols, Complex Structures of Symbols, and Processes that Operate on Symbols (Newell & Simon, 1976), the central thesis of his writings states, "Conventional computers can be, and have been programmed to represent symbol structures and carry out processes on those structures in a manner that parallels, step by step, the way human brain does it.".
In his writings he (Simon) basically argued that every angle of human mind is translable into definable representations, and the definition can be translated and embedded to a thinking machine. In order to defend his thesis, he explained several important points of disscussion such as:
- The Concept of Decomposable System
- Two Approaches of Artificial Intelligence (humanoid and non humanoid)
- The Concept of Mind from Psychological Perspective
- Selective Heuristic Search
- Recognition: The Indexed Memory
- Seriality: The Limits of Attention (short term memory)
- The Architecture of Expert System
In addition he also responded and defended his thesis to the disputes that people usually have regarding the angle of thinking that machine could not copy such as:
- Semantics
- Intention
- "Ill Structured" Tasks
- Language Processing
- Intuition
- Insight
- Creativity
At the end of this meeting we have a nice discussion about the state of the art of this writing (1995) and the condition that is happening now, and the extent of the applicability of the Simon's vision in the present and the future.
Wednesday, April 22, 2009
Wednesday, April 15, 2009
Meeting Minutes - 15 April 2009
Today I presented the paper “Computational Intelligence in Economic Games and Policy Design” by Herbert Dawid, Han La Poutré, and Xin Yao (IEEE Computational Intelligence Magazine, 3(4), 22–26, 2008). The paper provides an overview of applications of computational intelligence techniques in economics. Both strong and weak points of the use of computational intelligence techniques are discussed. According to the authors, the two most important weak point are the issue of empirical validation and the issue of robustness.
I also discussed my own view on the relation between mainstream economics on the one hand and agent-based computational economics on the other hand. Due to the increasing popularity of experimental economics and evolutionary game theory, mainstream economics focuses more and more on bounded rationality and dynamic (rather than static) analysis. From this perspective, the difference between mainstream economics and agent-based computational economics is smaller than is sometimes thought. I argued that the main difference is between following a mathematical approach (as mainstream economics does) and following a simulation approach (as agent-based computational economics does). There is much to be gained by combining these two approaches. Today’s meeting ended with a discussion of the difference between mathematical analysis and computer simulation, and how this difference relates to the difference between deduction and induction in science. We also discussed the issue of implicit assumptions that are hidden in technical details in agent-based computational economics research.
I also discussed my own view on the relation between mainstream economics on the one hand and agent-based computational economics on the other hand. Due to the increasing popularity of experimental economics and evolutionary game theory, mainstream economics focuses more and more on bounded rationality and dynamic (rather than static) analysis. From this perspective, the difference between mainstream economics and agent-based computational economics is smaller than is sometimes thought. I argued that the main difference is between following a mathematical approach (as mainstream economics does) and following a simulation approach (as agent-based computational economics does). There is much to be gained by combining these two approaches. Today’s meeting ended with a discussion of the difference between mathematical analysis and computer simulation, and how this difference relates to the difference between deduction and induction in science. We also discussed the issue of implicit assumptions that are hidden in technical details in agent-based computational economics research.
Thursday, April 9, 2009
Meeting Minutes - 25 March 2009
In the presentation, Romke and Otto presented the outcomes from the master seminar in computational economics. The research was focused on improving the price prediction mechanism of the MinneTAC agent. The MinneTAC agent is an agent-based computer model that is developed by the university of Minnesota in cooperation with the Erasmus University.
The MinneTAC agent competes with other Agent based computer models in the Trading Agent Competition for Supply Chain Management (TAC SCM). The TAC SCM game was designed to come to the best solution for an Agent based computer model that is capable of dealing with the problems of a dynamic supply chain.
In the MinneTAC agent, there is an ensemble, consisting of multiple price predictors used to predict the future market prices. The function of the model selection mechanism is to determine the most accurate price based on the predictions from all the individual predictors making up the ensemble. The advantage of using multiple predictors is the ability to capture more features in the data then a single predictor. The disadvantage of using multiple predictors is that different features are captured that causes different predictions. A second disadvantage is that not every price predictor is performing optimal for every time horizon and quantity of training data. To overcome these disadvantages, there is a dynamic weighting mechanism with adaptive weights developed for the MinneTAC agent. This weighting mechanism has to find to the optimal weights for every price predictor for every time horizon. The weights are learned during the game, while the agent is competing with its competitors for customer orders. When the agent starts, every price predictor has an equal weight. During the game, the MinneTAC agent starts using the optimal weights. This means that the price prediction mechanism is not working with the optimal weights during the first phase of the game.
In our seminar research, we found the optimal weights for every price predictor during the game. This data is used to bootstrap the agent to increase the performance in the first phase of the game.
R.J. Romke de Vries
O.B. ter Haar
The MinneTAC agent competes with other Agent based computer models in the Trading Agent Competition for Supply Chain Management (TAC SCM). The TAC SCM game was designed to come to the best solution for an Agent based computer model that is capable of dealing with the problems of a dynamic supply chain.
In the MinneTAC agent, there is an ensemble, consisting of multiple price predictors used to predict the future market prices. The function of the model selection mechanism is to determine the most accurate price based on the predictions from all the individual predictors making up the ensemble. The advantage of using multiple predictors is the ability to capture more features in the data then a single predictor. The disadvantage of using multiple predictors is that different features are captured that causes different predictions. A second disadvantage is that not every price predictor is performing optimal for every time horizon and quantity of training data. To overcome these disadvantages, there is a dynamic weighting mechanism with adaptive weights developed for the MinneTAC agent. This weighting mechanism has to find to the optimal weights for every price predictor for every time horizon. The weights are learned during the game, while the agent is competing with its competitors for customer orders. When the agent starts, every price predictor has an equal weight. During the game, the MinneTAC agent starts using the optimal weights. This means that the price prediction mechanism is not working with the optimal weights during the first phase of the game.
In our seminar research, we found the optimal weights for every price predictor during the game. This data is used to bootstrap the agent to increase the performance in the first phase of the game.
R.J. Romke de Vries
O.B. ter Haar
Thursday, March 12, 2009
Meeting Minutes - 11 March 2009
At this meeting Milan presented his paper "Overconfident Investors in the LLS Agent-Based Artificial Financial Market," as a preparatory talk for the upcoming IEEE SSCI CIFEr 2009 conference.
This paper is a part of the relatively new research stream where agent-based models of financial markets are used to study various topics of behavioral finance. It has been recognized in the literature that such models could be very suitable to make a link between the behavioral biases of individual investors and the aggregate market phenomena, such as the dynamics of the market prices.
In this paper we focus on overconfident investors, and model overconfidence as miscalibration, in such a way that investors are too certain in their predictions of future returns of a risky asset. In the methodological sense, this paper follows an incremental approach, where an existing model (Levy, Levy, Solomon, 2000) is modified to study the consequences of introducing a variation in investor behavior, namely the overconfidence bias.
We find that overconfident investors create less frequent, but more extreme bubbles and crashes in the market, compared to the original model. Furthermore, more overconfident investor introduce more excess volatility of the market price (over the volatility of the fundamental price), and also reduce the trading volume (as they are highly invested in stock during bubbles). Since this is a rising market, overconfident investors tend to take a larger share in the total wealth of all the market participants.
Furthermore, we study the emergence of overconfidence through biased self-attribution. Investors, who attribute successful predictions to their own skill and unsuccessful predictions to bad luck, learn quickly to be overconfident, and remain at such a high level of overconfidence. For unbiased self-attribution, the level of overconfidence varies greatly depending on the success of predictions.
During the meeting we had a fruitful discussion about the implications of these results, and there were also very interesting suggestions for the future research. In the light of the recent developments of the real-world financial markets, it would be particularly interesting to study the consequences of investor overconfidence in a declining market.
This paper is a part of the relatively new research stream where agent-based models of financial markets are used to study various topics of behavioral finance. It has been recognized in the literature that such models could be very suitable to make a link between the behavioral biases of individual investors and the aggregate market phenomena, such as the dynamics of the market prices.
In this paper we focus on overconfident investors, and model overconfidence as miscalibration, in such a way that investors are too certain in their predictions of future returns of a risky asset. In the methodological sense, this paper follows an incremental approach, where an existing model (Levy, Levy, Solomon, 2000) is modified to study the consequences of introducing a variation in investor behavior, namely the overconfidence bias.
We find that overconfident investors create less frequent, but more extreme bubbles and crashes in the market, compared to the original model. Furthermore, more overconfident investor introduce more excess volatility of the market price (over the volatility of the fundamental price), and also reduce the trading volume (as they are highly invested in stock during bubbles). Since this is a rising market, overconfident investors tend to take a larger share in the total wealth of all the market participants.
Furthermore, we study the emergence of overconfidence through biased self-attribution. Investors, who attribute successful predictions to their own skill and unsuccessful predictions to bad luck, learn quickly to be overconfident, and remain at such a high level of overconfidence. For unbiased self-attribution, the level of overconfidence varies greatly depending on the success of predictions.
During the meeting we had a fruitful discussion about the implications of these results, and there were also very interesting suggestions for the future research. In the light of the recent developments of the real-world financial markets, it would be particularly interesting to study the consequences of investor overconfidence in a declining market.
Thursday, February 26, 2009
Meeting Minutes - 25 February 2009
Today I presented my research done in the context of my master's thesis. I presented a novel product pricing approach for the TAC SCM game, where products are sold through reverse auctions with sealed bids (i.e., traders bid on customer requests for quotes and cannot observe their competitors' pricing behavior).
The new approach is based on price distribution estimations, where the relation between on-line available data and distribution parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression. These probabilities that a customer accepts a price offered by a trader can be used to determine the price yielding a desired quota. The approach has been implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. The novel approach significantly improves performance; more orders are obtained against higher prices. Profits more than double.
During the presentation, (adaptations to) the game specifications of the TAC SCM game were discussed in the group. We briefly discussed the possible impact of new entrants during a game, more competitors, and a randomized game length. Apparently, according to game theory, end-of-game effects would be reduced when the game end is randomized.
Alexander
The new approach is based on price distribution estimations, where the relation between on-line available data and distribution parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression. These probabilities that a customer accepts a price offered by a trader can be used to determine the price yielding a desired quota. The approach has been implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. The novel approach significantly improves performance; more orders are obtained against higher prices. Profits more than double.
During the presentation, (adaptations to) the game specifications of the TAC SCM game were discussed in the group. We briefly discussed the possible impact of new entrants during a game, more competitors, and a randomized game length. Apparently, according to game theory, end-of-game effects would be reduced when the game end is randomized.
Alexander
Thursday, February 19, 2009
Meeting Minutes - 18 February 2009
Today I discussed the main results of my master's thesis, in which I try to incorporate procurement information into an economic regime model based on sales information. This model is used in the MinneTAC agent, which is an artifical trading agent that competes in the TAC SCM game.
First, I gave a brief description of the TAC SCM game, after which I introduced the regime model as it is currently used in the MinneTAC agent. Then, I introduced a new procurement variable, i.e., offer prices, after which I elaborated on both regime identification and regime prediction. Finally, I discussed some experimental results.
During the last part of the presentation where I introduced some experimental results, we had a discussion on the causes of these results. As it seems, implementing procurement information into the regime model does not lead to better performance of the MinneTAC agent in TAC SCM games. In fact, the agent gets more orders from customers, but generates lower profits. Other competitors seem to take advantage of the situation, since their profits increase when MinneTAC uses the new regime model.
A suggested cause is the fact that there could be a delay regarding the procurement information. Procurement information might be a leading indicator for regimes, so perhaps creating a regime model based on the sales price of yesterday and the procurement offer price of for instance three days earlier could improve the performance of the agent. However, this is dependent on the cost allocation of the agent. Furthermore, there is a lack of adaptivity in the regime model and therefore, the model cannot adjust properly during a game. Finally, regime information is not used for price setting in the experiments discussed today. Establishing this connection between regimes and price setting could improve the overall results as well.
Frederik
First, I gave a brief description of the TAC SCM game, after which I introduced the regime model as it is currently used in the MinneTAC agent. Then, I introduced a new procurement variable, i.e., offer prices, after which I elaborated on both regime identification and regime prediction. Finally, I discussed some experimental results.
During the last part of the presentation where I introduced some experimental results, we had a discussion on the causes of these results. As it seems, implementing procurement information into the regime model does not lead to better performance of the MinneTAC agent in TAC SCM games. In fact, the agent gets more orders from customers, but generates lower profits. Other competitors seem to take advantage of the situation, since their profits increase when MinneTAC uses the new regime model.
A suggested cause is the fact that there could be a delay regarding the procurement information. Procurement information might be a leading indicator for regimes, so perhaps creating a regime model based on the sales price of yesterday and the procurement offer price of for instance three days earlier could improve the performance of the agent. However, this is dependent on the cost allocation of the agent. Furthermore, there is a lack of adaptivity in the regime model and therefore, the model cannot adjust properly during a game. Finally, regime information is not used for price setting in the experiments discussed today. Establishing this connection between regimes and price setting could improve the overall results as well.
Frederik
Thursday, February 12, 2009
Meeting Minutes – 11 February 2009
Today I presented some preliminary results of a research project that is intended to be part of my PhD thesis. I discussed an economic model of price competition between spatially organized firms. I discussed the Nash equilibrium solution of the model, but most of the time I focused on the outcomes achieved by so-called evolutionary dynamics. These outcomes were analyzed mathematically (partly analytically, partly numerically) but also through computer simulations. Interestingly, on average firms turned out to cooperate (or collude) with each other under the evolutionary dynamics.
We had a very fruitful discussion about the conditions necessary/sufficient to achieve the cooperative outcome, focusing in particular on the plausibility of various assumptions. We also discussed on a general level the methodology typically used in economic/game-theoretic research.
Ludo
We had a very fruitful discussion about the conditions necessary/sufficient to achieve the cooperative outcome, focusing in particular on the plausibility of various assumptions. We also discussed on a general level the methodology typically used in economic/game-theoretic research.
Ludo
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