Why is decision coordination an important problem? Decision support is especially critical when human decision makers are faced with combinatorial problems, uncertain and partially-visible data, and our built-in cognitive biases. When the results of decisions in different domains interact with each other, we have an additional level of complexity that is easy to ignore.
We use the TAC SCM simulation to model complex, interacting decisions, and the fully-autonomous MinneTAC trading agent to test our ideas for decision support. The agent must trade in two competitive markets simultaneously, while managing its own inventory and production facility. A successful agent must buy parts it needs to manufacture and sell finished products, and it must sell its finished products at a profit. Inventory is relatively costly, competition is stiff, and agents that cannot effectively coordinate their decisions are easily defeated.
We reviewed the coordination strategies employed by a number of successful agents. These include a "sales pull" method, inventory-centric approaches, a production-centric approach that fills its future production schedule with the products that are expected to give the highest marginal profit, an approach that projects a "target demand" into the future that is expected to satisfy profit targets, and a multi-layer system of internal "markets" in which projected customer demand bids on products, which in turn bid on parts and production capacity, etc.
We wrapped up by looking in some detail at the behaviors of the two top agents in the 2009 competition, DeepMaize from the University of Michigan and TacTex from the University of Texas. They were very nearly tied, although TacTex bought and sold considerably more volume and carried much larger inventories. We looked at an example where TacTex built up a large finished-goods inventory during a period of low customer demand, when parts are inexpensive, and used that inventory to keep prices depressed during a later period of high demand, when other agents were competing for parts and were consequently squeezed for profits. This is clearly a very risky strategy, but we assume that the TacTex team has used its machine-learning expertise to recognize the market signal patterns that indicate a reasonable probability of such a situation occuring. The regime model used by MinneTAC could presumably predict such a situation also, but so far it's not being used to drive strategic decisions.
Tuesday, October 20, 2009
Friday, May 29, 2009
Meeting Minutes - 27 May 2009
At 27th May 2009, we have 2 guest researchers, Xiaoyu Mao and Ducco Ferro, from Almende, a research company that focusses it research in multi-agent system. Both were discussing their papers that they will present at AAMAS.
Xiaoyu Mau presented an application of agent technology in the airport operation entitled "Heterogeneous MAS Scheduling for Airport Ground Handling", while Ducco presented his paper entitled "The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks". That was a great meeting with great crowd also.
Xiaoyu Mau presented an application of agent technology in the airport operation entitled "Heterogeneous MAS Scheduling for Airport Ground Handling", while Ducco presented his paper entitled "The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks". That was a great meeting with great crowd also.
Thursday, May 7, 2009
Meeting Minutes - 6 May 2009
At 6th of May, we have a guest speaket from TU-Delft. His name is Mathijs de Weerdt. He presented his ACM paper regarding the Qualitative Vickrey Auction concept. A concept at which bidders and auctioneers (centers by his definition) put the agreement of the bid not based on the monetary value (highest or lowest price) but based on the offer demand attributes suitability rank.
He explained this concept in the reversed auction environment example. A concept at which the auctioneer has already defined the rank of offers preferences (the offer contains of many attributes). And the bidder that submits the offer of highest rank wins. The winner bidder then can choose to deliver the offer ranges from his bid point offer to the second highest bidder point offer (Vickrey Auction).
Nice concept, based on the attributes rank preferences. For more explanation you are welcomed to browse our site to download the paper directly (http://large.rsm.nl/meetings.xml).
He explained this concept in the reversed auction environment example. A concept at which the auctioneer has already defined the rank of offers preferences (the offer contains of many attributes). And the bidder that submits the offer of highest rank wins. The winner bidder then can choose to deliver the offer ranges from his bid point offer to the second highest bidder point offer (Vickrey Auction).
Nice concept, based on the attributes rank preferences. For more explanation you are welcomed to browse our site to download the paper directly (http://large.rsm.nl/meetings.xml).
Wednesday, April 22, 2009
Meeting Minutes - 22 April 2009
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.
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 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.
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