Information perception and price dynamics in a continuous double

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Information perception and price dynamics in a continuous double auction Juliette Rouchier and Stéphane Robin Simulation Gaming 2006; 37; 195 DOI: 10.1177/1046878106287947 The online version of this article can be found at: http://sag.sagepub.com/cgi/content/abstract/37/2/195

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Information perception and price dynamics in a continuous double auction Juliette Rouchier Stéphane Robin Centre National de Recherche Scientifique

The authors combine two methodologies—experimental economics and agent-based simulation—for the study of rational behavior of individuals in a market environment. The related market represents a competitive continuous double auction (CDA). Much of what is known about CDA is based on experimental research. The choice processes observed during behavior experiments are still not understood sufficiently. Therefore, in this article, the authors build an artificial society to analyze how perceptions of the market price affect individual strategies and collective behavior. More precisely, using an analytical science approach, they study learning (e.g., number of transactions memorized) and use of global versus local information. The result of their work mainly shows that the memory of past transactions is of little importance and that the revision of prices models the memory of agents adequately to demonstrate how rapidly price converges on an equilibrium state of the market. KEYWORDS: agent-based simulation; continuous double auction; experimental economics

One scientific objective of empirical research is to discover regularities in areas where existing theories still have little to offer. It is amazing that economic theories do not elaborate on individual behavior in the market (Kirman, 1997). In this article, we explain how we combine two methodologies, experimental economics and agentbased simulation, to study the rational behavior of individuals in a market environment. This study would help us analyze how individuals get information from the market prices and how this information matters for their decisions and, hence, market dynamics. Experimental economists bring to the fore how the exchange institution, which embodies the set of rules that organizes a market, is a crucial variable to set its dynamics. Among numerous market institutions, the continuous double auction (CDA) has received special attention from the experimentalists because of its remarkable properties, now well documented (Holt, 1995). Individuals with local information about their preferences, trading through CDA trading rules, collectively extract the maximum exchange surplus from the market. What we know about CDA comes from results of experimental laboratory research. The theoretical basis is still weak. In fact, economists are only beginning AUTHORS’ NOTE: The authors thank Jan Klabbers for his thoughtful comments. Juliette Rouchier thanks JSPS for financing her research and Toru Ishida, who invited her since 2004. SIMULATION & GAMING, Vol. 37 No. 2, June 2006 195-208 DOI: 10.1177/1046878106287947 © 2006 Sage Publications

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to build a theory of trading strategy and price formation. Choice algorithms have been proposed, either to establish a theoretical basis of information processing (Easley & Ledyard, 1993) or to build simple simulations (Gode & Sunder, 1993) or more elaborate ones involving actual learning by agents (Brewer, Huang, Nelson, & Plott, 2002). A precise matching of agents’ and humans’ behavior has not yet been possible in simulations; either global or individual histories are matched (Janssen & Ahn, 2003). However, simulation, models will help identify those parameters that are irrelevant with respect to representing rationality. In this article, we analyze individual economic behavior to generate hypotheses about rational behavior of individuals. We start from experimental observations of CDA markets. Then, we build artificial societies, which allow us to analyze how individual perceptions of the market price affect individual strategy and collective behavior. Our approach fits into the analytical science domain (Klabbers, 2006 [this issue]). To study rational behavior, we arrange several simulations, each time making small changes in the parameters. Observed behavior gives an idea of the logical consequences of the defined rationality in the given institutional context. The differences between simulation results show the effect of the parameters on the collective learning process. This enables us to demonstrate which parameters are relevant or irrelevant to define agents’ rationality. Here, we concentrate on one type of information, assuming that agents (who can either accept or make a proposal) can use past prices when making their choice for transactions. From this simple assumption, we study the effect of quantity of learning (number of transactions memorized) and use of global versus local information. While studying the experimental results, we follow a twostep procedure that allows us to compare our artifact with a human experiment. First, we observe in each simulation (and experiment) the individual trajectories. Second, for each simulation run, we analyze a few parameters to check for which parameter values of the simulation come close to actual human behavior. The results of our work mainly show that the memory of past transactions is of little importance and that the revision of prices represents the memory of the agents strongly enough to get to a credibly quick interperiod convergence into a steady state. The rest of this article is organized as follows. We first discuss the complementarities of experimental economics and agent-based simulation for analytical analysis of dynamical processes. The next section is dedicated to CDA presentation and laboratory results. We then present briefly the model we built, which helped us focus on the issue of perception for agents (see also Rouchier & Robin, 2004).

Experimental economics and multiagent models The comparison of experimental economics with artificial societies, based on multiagent modeling, is rapidly drawing attention in economics. In both fields, researchers are keen to identify, model, and test the actual behavior of individuals making economic choices. For market analysis, both methodologies allow a strict control of environmental variables such as market institution, individual preferences,


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and individual information. They mainly differ in their views on individual behavior and rationality. Experimentalists deal with real participants, real human behavior, and imperfect rationality. Simulation modelers deal with simulated agents: automatonlike behavior based on rule-driven rationality. Agent-based simulation methods provide an opportunity to test different assumptions about individual perceptions and decision rules. With the experimental method, this is much less easy to perform. However, agent-based models are more difficult to validate. We use agent simulations in a looping process. We model assumptions about a given reality (observed in the laboratory) into a simulation. The results of simulation runs are validated with data from real life or from behavioral experiments. Following such an approach, aimed at making explicit the relations between variables, experiments and simulation runs are used as complementary methods. This understanding is the first step to a microeconomic theory of market.

A case study: CDA market CDA institutional setting Markets are interesting settings to study rationality in a context of nondirect communication and local information. Here, independent individuals on the market have a clear set of preferences, which means from an economic viewpoint: a limit value for buying or selling goods and an incentive to buy or sell. Such a condition offers a very efficient way to coordinate their behavior. Under such market conditions, individuals can get information about the others thanks to the observation of a minimum number of variables. It is a decentralized mechanism to coordinate the allocation of resources (Smith, 1982). Among diverse market institutional settings, the competitive continuous double auction has been shown to be very efficient, in that it quickly produces the highest value of global surplus for the agents involved, when they enter the market with clear preferences. Clear preferences mean that buyers know the maximum limit price they can pay, and sellers, the minimum limit price required to sell. The CDA is a two-sided progressive auction. At any moment, buyers can submit bids (offers to buy). Similarly, sellers can submit asks (offers to sell). Both buyers and sellers may propose an offer or accept the offer made by others, representing the counteroffers in the market. If a bid or ask is accepted, a transaction occurs at the offer price. An improvement rule is imposed on new offers entering the market, requiring submitted bids (/ asks) at a price to exceed (/ be less than) the standing bid (/ ask). Each time an offer is satisfying for one of the participants, he or she announces the acceptation of the trade at the given price, and the transaction is completed. Once a transaction is completed, the market is cleared (meaning that there is no standing bid or ask anymore) and the agents who have traded leave the market. At that moment, like at the very opening of the market, the first offer can take any value, and this proposed price imposes a constraint on any following offer. When the


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market closes, at a moment of time decided beforehand, agents who have not yet traded are not allowed to continue. In this market institution, all market events (bid, ask, acceptance, and time remaining before market closing) are observed by all. CDA market experiment In experiments, participants are usually divided in two groups of traders: buyers and sellers. Their preferences are controlled through limit prices, which are local information (Smith, 1976). Studying auctions, we consider that agents have strong incentive to exchange (there is an opportunity cost associated with failing to exchange). In a standard protocol for market experiments, participants are paid proportionally to the exchange surplus they obtain from the market. Quite the reverse, in our experiments, participants do not get any reward. Usually, an experiment on a CDA market is organized as a succession of market periods where the same situation applies at the beginning of each market, participants being given exactly the same good to sell or buy. Figure 1 illustrates the contract prices for each trading period of a typical CDA experiment. What is observed is the evolution of prices of transaction over time. The diagram indicates how well participants adapt to the opportunities offered by the market setting. In most experiments, there is a global “learning” taking place at two levels: During each market period, the prices converge to the theoretical equilibrium; from one period to the other, the initial transaction prices get closer to that equilibrium (as can be seen in Figure 1). Evaluation of the efficiency of the market results from the comparison between the surplus made by the individuals and the best surplus they could get. The CDA market leads to higher market efficiencies than other market settings (Holt, 1995). Figure 1 describes the results of an experiment with four sellers and four buyers, trading for 15 periods. Vertical lines represent the opening and closing moments of market periods. The horizontal line represents the equilibrium value, representing the steady state value of the market system. Each dot is a bid or an ask that is expressed as a proposed transaction price. Actual transactions occur when a bid, proposed by a buyer, or an ask, proposed by a seller, is accepted by the other party. Red circles that are linked together indicate the prices of these transactions (results from experiments by Robin, with students of the ENSGI school in Grenoble in 1999). These results can be seen as robust because they correspond with those from various similar experiments, where individuals participate in a succession of markets. The results fit with the outcomes of a small number of participants (Smith & Williams, 1989), with an environment that is unstable due to unpredictable shifts of demand and supply (Jamison & Plott, 1997), and even with some added transaction costs, covering participation in the market (Noussair, Robin, & Ruffieux, 1998). When one sees prices converge toward the equilibrium price, without agents having any information about what the price should be, then we observe that collective learning is taking place. The underlying learning processes are not yet well understood. Some even speak of a complete mystery. Therefore, it is worthwhile to detect which data are important



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FIGURE 1: Bids, Asks, and Prices of Transactions as a Function of Time

for the actors to make their choices and which data are not relevant. By means of agent-based simulation methods, we will explore how agents deal with market prices and how their subsequent actions affect the pricing process. The agent-based model performs the function of a mini-theory that is being tested. CDA and simulation As far as simulation is concerned, one seminal article by Gode and Sunder (1993) shows the strength of institutional constraints on the actions of agents and, hence, the convergence of prices. Other researchers building models of CDA often vividly criticize their work, because their approach is not focused on rationality (they use extremely simple agents) and, for this reason, they can only enlighten very specific institutional settings, the ones they explore. Their approach is quite original in the behavioral economics literature in the sense that it is close to an “artificial life approach.” They do not pretend to study human rationality. They focus on the abstract reproduction of phenomena, and this is why their results cannot be generalized to diverse settings (Brenner, 2002). Their work, however, gives us a good idea of what the CDA market necessarily produces. Therefore, we will evaluate our model from the perspective of its added value to understand that CDA market reality.


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We use our simulations to test the theory we developed, based on data from experiments, by embedding it in the agent-based model. We perform an iterative process of tuning the model parameters to fit experimental data, to increase our knowledge and understanding of human rationality. We wish to explain the convergence of prices in the subsequent CDA markets: from one market opening to the following one. We look for plausible shapes of convergence, close to the time series data from experiments. In addition, we perform sensitivity analyses (small changes in our parameter setting) to evaluate which factors can be of real importance. This is the main reason why we developed the multiagent simulation model, which enables us to explore different “cognitions” of agents. Our choice is to start with a very simple form of cognition, not yet including actual reinforcement learning (Brenner, 2002) or even more complex learning algorithms. Our assumption is that the CDA mechanism demonstrates such important converging properties that our agents might be able to perform well, even if they lack subtle representations of the world. As we point out in the next section, our agents are only driven by average prices. They do not use more complex information such as frequency of occurrence of a price transaction (as a classical reinforcement model would use) or frequency of transactions that could show if the market is active or not. In our case, we study two parameters that are part of agents’ cognition: • perception: using past transaction prices as a reference. The agent can focus on local (its own past transactions) or global information (all transactions). • memory length (number of transactions used to calculate average).

The multiagent simulation model Assumptions and model The following multiagent model represents an artificial society, in particular, a competitive continuous double auction market. The model represents a CDA market, made up of X sellers and X buyers, each being allocated a limit value that constrains their transaction price. Here, there is only one type of good that is exchanged and each agent possesses only one unit of that good. A simulation is made of successive market periods, where agents have the same limit values every time. During one period, the auction is defined in a noncontinuous way, as a succession of steps: For one step, buyers announce their “ask” or “accept” transaction; for a second step, sellers announce their bid or acceptance. This succession, easier to implement than a continuous auction, has been shown to be a good approximation of a continuous situation (Gode & Sunder, 1993). The number of steps of a period can be understood as the total duration (TD) of the period. At each moment (or “step”), the market is defined either by a couple of outstanding bids and offers, which constrain the agents’ proposals, or by a cleared situation, with any proposal being acceptable. Each agent is endowed with a limit value (LV), which remains unchanged during the simulation. To describe agents’ rationality, we were inspired by Easley and Ledyard’s (1993) representation of decision making even if their article has no dynamical setting.



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This representation seemed to us to be simple enough to track the choices of the agents (from a simulation point of view) and to be qualitatively coherent with our knowledge of participants’ behavior in the experiments. Two evolving parameters are used to describe rationality of agents, each being individual and private: reservation price (RP) and stress time (ST). ST is defined as a time-step in the market period, after which an agent will potentially revise its RP at each time-increment until the end of the market. It is an evolving probability as mentioned below. It is a way of representing the stress occurred by real actors who are aware of the end of the market. A market is organized by the succession of bids and offers by agents. At each moment, only agents, whose RP is included between the outstanding bid and offer, can make or accept a proposal. Among them, one agent is randomly chosen to propose its RP. For RP revision, we introduce a limited perception about information provided on the market. At each moment, the agent has an image of the market, which is defined by an average price (AP). That AP depends on memory length (ML; number of transactions taken into account to calculate the AP) and perception (P; global or local information), which are both common parameters. An agent is hence defined by RP and ST (both evolving), ML and P (both stable). It is included in an environment where there is some time left (TL) before the end of the market and before transactions take place. After its ST, an agent revises his or her RP with a probability that increases as TL decreases, such as Equation 1 shows. Prob = TL / (TL – ST).

(1)

Then the revision happens (here is the algorithm for a buyer, the revision of a seller being symmetric, with the value decreasing; see Equations 2 and 3): AP ≤ LV ⇒ newRP = AP.

(2)

AP ≥ LV ⇒ newRP = RP + (LV – RP / TL).

(3)

These equations model the rule-driven behavior of the agents in the CDA market. RP and ST evolve from one period to another as follows. If the agent has made a transaction, then RP takes the transaction price value and ST increases randomly (hence, stress will occur later). If the agents have not made a transaction, then RP stays the same and ST decreases randomly (stress will occur earlier). Both RP and ST are attributed randomly at the beginning, with the only constraint that RP is always smaller than the limit price for buyers (and higher for sellers). A more precise description of the model is available in Rouchier and Robin (2004). Simulations: Observations In our multiagent model, all simulations encompass the same number of agents and the same LVs. Different simulations are defined by (a) type of perception (local


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or global information), (b) the memory length, and (c) the fact that agents will or will not revise their ST. In a previous article (Rouchier & Robin, 2004), we presented some simulations of the model, showing how the prices and individual trajectories, in general, qualitatively fit the behavior of the participants in the experiments. We also identified that for each initial condition, the results showed a similar behavior pattern. We then realized that simulations were not differentiated by qualitative results: Markets were rather similar whatever memory length and perception. Hence, we gathered that two quantitative factors made a difference among the simulations: price convergence speed and efficiency convergence. Next, we describe more systematically results that link the influence of perception and memory length to the shape of convergence. We run the simulation for 50 markets, where 50 agents have 50 periods to exchange bids, with 500 steps in each period. The perception of agents is based on either local or global information. The value of memory length goes from 1 to 400, which represents the highest number of transactions that can take place in 10 succeeding markets. Given the LVs set by the agent, the competitive equilibrium exists at any market price of between 790 and 800 and a quantity of 40 transactions. As in the previous example, we expect to produce results where • the use of global knowledge will speed up price convergence and efficiency, as compared to the use of local information. • an increased number of transactions memorized will also speed up convergence to an equilibrium or steady state condition of the market.

The maximum surplus for a market is the surplus made if all possible transactions took place at equilibrium price. Efficiency is calculated as Eff = total surplus made on the market / maximum surplus.

Hence, it is an indicator of the number of transactions performed and of price convergence. Speed of convergence is measured through the evolution of the standard deviation of contracts from competitive equilibrium market price: Q 1 SP ∗ (t) = (Pi − P ∗ )2 , Q i=1

(4)

where Pi is the ith contract price in market period t, Q is the total number of contracts in period t, and P* is the competitive equilibrium market price. To estimate



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how rapidly this standard deviation converges to zero, we use the following model (Smith & Williams, 1982; see Equation 5): Sp*(t) = αe−βt + εt.

(5)

The beta term measures the speed of convergence. If it is positive, then deviation from competitive equilibrium market price goes toward zero and an increase of this term expresses an increase of the speed of the convergence. The comparison of speed of convergence is thus made with the comparison of this term in different simulation treatments. We first look at the “internal analysis of results”—where simulations are compared with each other according to the stated criteria (price evolution, convergence speed, and efficiency)—and, hence, we check if the increase of information, in our context, helps to make better use of the market institution. We can then move to an “external validity criterion,” which compares the data from the simulation with those gathered from the experiments. In the proposed market, with the LVs stated, prices and efficiency summarize most of the information on agents’ behaviors. They show how agents exchange bids. They illustrate how those who are in a good position actually make transactions or are left behind (showing little learning). Main results As observed in the experiment, the CDA in the simulation is amazingly efficient. The market prices rapidly converge and are close to the competitive equilibrium. Efficiency is more than 90% (see Figure 2). Roughly, this rapid convergence is observed whatever the treatment parameters of the simulation. The precise analysis of the speed on convergence shows a significant effect of the revision of ST parameter treatment (see Figure 3). The revision of time stress has a significant effect on the speed of convergence. During the first periods, and for each treatment, we observe a rapid decrease of the standard deviation of contracts from the competitive equilibrium market. The deviation still decreases for the simulation without revision stress. For the simulation with revision stress and especially for the simulation with local information with revision stress, the contracts observed fail to converge to the competitive equilibrium market price. The analysis of the speed of convergence beta indicator shows a significant difference (p < .01) between simulations with revision stress (β = –.09 for global information and β = –.07 for local information) compared with simulations without revision stress (β = –.03 for global information and β = –.02 for local information). In fact, the analysis of the parameters obtained through the model of convergence for simulations without revision stress shows that there is, at least, no convergence at all for simulation with revision stress. The speed of convergence increases slightly in the case of global compared with local information. Nevertheless, a variance t test fails to reject that the indicator for this speed of convergence is different for global


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Time FIGURE 2: Prices of Transactions as a Function of Time for a Simulation Without Time Stress Revision and Memory Length of 10 Transactions NOTE: Vertical lines represent opening and closing moment of market periods. The horizontal black line represents the equilibrium value.

and local information. One could expect a stronger effect of this variable as well. This result is clearly different from what experimentalists observe in the laboratory with real people. Actually, it is possible to consider that CDA with local information is equivalent to a decentralized bilateral negotiation market institution. And usually, markets organized with this last setting fail to converge to the competitive equilibrium and are inefficient. Finally, it is surprising that we do not observe any influence of the memory length on the speed of convergence. For each treatment, the speed of convergence is not correlated to memory length. In case of very short memory, the speed of convergence observed is comparable to the one with a maximum of memory (including all transactions in the 10 previous market periods). With regard to market efficiency, artificial agents are able to extract more than 95% of the exchange surplus from the first periods of the simulation. However, markets fail to stabilize at the maximum efficiency when time stress is revised. Moreover, in this setting, we observe temporary reduction of the efficiency ratio with period repetition. The average market efficiency ratio for each period and each treatment are presented in Figure 4.



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FIGURE 3: Evolution of the Average Standard Deviation of Contracts From Competitive Equilibrium Market Price for the Different Simulation Treatments

The market efficiency with time stress revision is significantly low compared with the maximum obtained without stress revision. The ability of agents to reduce time stress, if they exchange information during the previous period to wait for a better price, drives to inefficiency with respect to the reduction of the number of units exchanged and a decrease of surplus produced by the market.


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100.0%

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88.0% Period Global with time stress rivision

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FIGURE 4: The Evolution of Average Market Efficiency Ratio for Each Treatment

Discussion Our results are very interesting in relation to the findings of Gode and Sunder (1993, 2004). First, contrary to Gode and Sunder, we are able to reproduce, with simple agents, the intermarket convergence. This result is true for any value of memory length and any type of perception. This means that just incorporating past transaction knowledge in the evolution of RPs is sufficient for the agents to be efficient in moving from one market to another. The other interesting result is that by making agents more intelligent, we diminish their capacity to effectively generate price convergence in the market. By “added intelligence,” we mean the ability of agents to reduce or increase time stress. It refers to their ability to deal with risk. If they succeed in making transactions, we interpret this as a nonrisky task. If agents do not reduce their price rapidly, such behavior implies that they are less efficient, until they learn to be more risk averse. In economic terms, such behavior shows the reasons for reduced market efficiency. This is quite a strange result in terms of economics, because it was not easy to make such a market to be less efficient within one market setting. From a system analytical viewpoint, this result offers the ability for the market to be very stable, because even with disruptions in the course of action due to agents performing suboptimal exchanges, and hence providing bad information to the other party, the market still converges toward equilibrium price.



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With this model, we are able to explore two dimensions of perceptions of agents in a CDA market. Even if we were not trying to build a very elaborate representation of the market, we chose the most obvious learning pattern for agents—average past prices—and we showed that this variable is not so relevant to induce price convergence. We built a simulation protocol—a multiagent model—that was inspired by common experimental economics. For that reason, to assess the credibility of our model, we used similar indicators both for the simulation model and for the behavior experiments. During the first part of our work, we had to demonstrate that our agent-based model could produce credible histories (time series data) for agents and also could identify regular behavior patters for the simulations, starting with the same initial values. Then, we produced a systematic comparison of results, based on aggregated data for each simulation, which was, for us, the only way to distinguish between various simulation runs. Eventually, this systematic comparison enabled to show the limited relevance of the rationality parameters we had defined (perception and memory length). Initially, they seemed to us intuitively very relevant. Nevertheless, we were able to demonstrate that some results are counterintuitive, yet meaningful. For us, this is a sign that our study, dealing with rationality of agents in markets, is relevant and worthwhile. We have shown that the experimental and simulation approaches we applied are complementary to each other. The observations made during a series of experiments helped us to formulate assumptions about procedural rationality of individuals, by identifying which variables could be important for participants to influence their decisions. While performing simulations, we were able to test our assumptions. Even if agents are not perfect representations of individuals, in simple settings, they can be modeled to demonstrate similar behavior. The various outcomes of different simulations enhanced the calibration and validation of the theoretical model. The use of simple agents enabled us to trace the reasons that certain behaviors are chosen and helped to explain differences when they emerged. If no differences in behavior can be spotted with the type of indicators we used, it could lead to the conclusion that the model is insensitive to the related parameters and that maybe they are irrelevant. That, in itself, is an important outcome of the methodology we have applied.

References Brenner, T. (2002). A behavioural learning approach to the dynamics of prices. Computational Economics, 19, 67-94. Brewer, P., Huang, M., Nelson, B., & Plott, C. (2002). On the behavioural foundations of the law of supply and demand: Human convergence and robot randomness. Experimental Economics, 5, 179-208. Easley, D., & Ledyard, J. (1993). Theories of price formation and exchange in double-oral auction. In D. Friedman & J. Rust (Eds.), The double auction market: Institutions, theories, and evidence (pp. 63-97). Reading, MA: Addison-Wesley. Gode, D. K., & Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Markets as a partial substitute for individual rationality. Journal of Political Economy, 101, 119-137. Gode, D. K., & Sunder, S. (2004). Double-auction dynamics: Structural effects of non-binding price controls. Journal of Economic Dynamics and Control, 28, 1707-1731.


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Holt, C. A. (1995). Industrial organization: A survey of laboratory research. In J. H. Kagel & A. E. Roth (Eds.), Handbook of experimental economics (pp. 349-444). Princeton, NJ: Princeton University Press. Jamison, J. C., & Plott, C. R. (1997). Costly offers and the equilibration properties of the multiple unit continuous double auction under conditions of unpredictable shifts of demand and supply. Journal of Economic Behavior and Organization, 32, 591-612. Janssen, M., & Ahn, T. K. (2003, March). Adaptation vs. anticipation in public-good games. Unpublished proceedings of Model to Model Workshop, Marseille, France. Kirman, A. (1997). The economy as an interactive system. In W. B. Arthur, S. Durlauf, & D. Lane (Eds.), The economy as an evolving complex system II (Studies in the Sciences of Complexity, XXVII, No. 2, pp. 491-531). Santa Fe, NM: Addison-Wesley. Klabbers, J. H. G. (2006). A framework for artifact assessment and theory testing. Simulation & Gaming, 37(2), ???-???. Noussair, C., Robin, S., & Ruffieux, B. (1998). The effect of transactions costs on double auction markets. Journal of Economic Behavior and Organization, 36, 221-233. Rouchier, J., & Robin, S. (2004, May 29-31). Looking for economic agents’ rationality in specific contexts: Double-auctions experiments and simulations. AESCS, Kyoto. Retrieved from http://www.ipe .media.kyoto-u.ac.jp/aescs04/ Smith, V. L. (1976). Experimental economics: Induced value theory. American Economic Review, 66(2), 274-279. Smith, V. L. (1982). Markets as economizers of information: Experimental examination of the “Hayek Hypothesis.” Economic Inquiry, 20, 165-179. Smith, V. L., & Williams, A. W. (1982). The effects of rent asymmetries in experimental auction markets. Journal of Economic Behavior and Organization, 3, 99-116. Smith, V. L., & Williams, A. W. (1989). The boundaries of competitive price theory: Convergence, expectations, and transaction costs. In L. Green & J. Kagel (Eds.), Advances in behavioral economics (Vol. 2). Norwood, NJ: Ablex.

Juliette Rouchier is a research associate in cognitive economics at the National Centre for Scientific Research (CNRS), affiliated with the GREQAM research institute, in Marseille. Her interest is to lead multiagent simulations and field studies on the topic of rationality in markets. She has been visiting Kyoto University from the end of 2004 until mid-2006. Stéphane Robin is a research associate in economics at the National Centre for Scientific Research (CNRS), affiliated with the GATE research institute, at the University Lumière Lyon 2. His research interests are experimental economics, behavioral economics, industrial organization, and market design. ADDRESSES: JR: GREQAM-CNRS, Centre de la Vieille Charité, 2 rue de la charite, 13002 Marseille, France; telephone: +33 (0)491917208; e-mail: [email protected]. SR: GATECNRS, Université Lyon 2, 93, chemin des Mouilles, 69131 ECULLY, France; telephone: +33 (0)472866083; fax: +33 (0)472866090; e-mail: [email protected].


Information perception and price dynamics in a continuous double

Information perception and price dynamics in a continuous double

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