ARTIFICIAL COORDINATION - SIMULATING ... - CiteSeerX

ARTIFICIAL COORDINATION SIMULATING ORGANIZATIONAL CHANGE WITH ARTIFICIAL LIFE AGENTS1

Torsten Eymann, Boris Padovan, Detlef Schoder

Albert-Ludwigs-Universität Freiburg Institute for Computer Science and Social Studies Telematics Department Friedrichstrasse 50 D-79098 Freiburg im Breisgau, Germany {eymann|padovan|schoder}@iig.uni-freiburg.de

The increasing use of information technology within and between companies yields changes in the predominant coordination mechanisms. On the one hand it is argued that we witness an overall shift towards market-like coordination of business transactions, whereas others argue that we face more hierarchy-like coordination. In this paper a multi-agent system is described that globally coordinates a multiple step value chain either way by using local optimization rules of self-interested autonomous agents. In further research it will be analyzed how changes in setup parameters (e.g. transaction costs, co-operational behavior) will effect the predominant coordination mechanism among the artificial life agents. Copyright © 1998 IFAC Economic Design, Agents, Artificial Intelligence, Optimization, Organizational Factors, Coordination

1. INTRODUCTION The increasing use of information technology within and between companies yields changes in their organizational structures as well as in the use of coordination mechanisms. In the literature the discussions about the „direction“ are rather controversial. Two extreme points of view illustrate that: On the one hand it is argued that we will witness an overall shift towards market-like coordination („move-to-market“- hypothesis) due to lowered transaction costs which relatively favor market over hierarchy. On the other hand we find arguments and empirical evidence that especially through the

deployment of IT between firms linkages become more and more intense which yields to an overall shift to hierarchy-like coordination („move-tohierarchy“-hypothesis) (Malone et al., 1987; Clemons and Reddi, 1993; Gurbaxani and Whang, 1995; Klein, 1996).

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Figure 1: Coordination Mechanisms between Markets and Hierarchies

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Figure 2: An example of a typical value chain from tree to desk Existing explanations start with a continuum between markets and hierarchical organizations (illustrated in Fig. 1), where one can find a world of discrete coordination mechanisms which in turn lead, in dependence on vertical integration or disintegration of economic activities, to different organizational structures. The coordination mechanism of negotiation between two or more cooperation partners probably leads over time to the institutionalization of a market (in economic terms); similarly, coordination by order (like in military groups) will probably lead to a strictly hierarchical organizational structure. Between these two extremes one can find a continuum of mixed forms which apply attributes from both. The research aim of the simulation described in this article is to investigate if, and to which extent, parameters like transaction costs, transparency of supply and demand, connectivity of market places, co-operational behavior and reputation of the participants influence the mechanism of coordination in either direction. With the use of artificial life agents, a primitive economic process is simulated - a value chain. Changing organizational patterns which appear when the agents trade with each other are visualized and taken as indication for the direction of the „move“, either to „markets“ or to „hierarchy“ or „anything goes“. This is performed by measuring the change of co-operational trade relations, where frequent switches are interpreted as market-like-coordination and infrequent switches as hierarchy-like-coordination. The parameters which are used for the experiment are identified by Klein (1996). The remainder of the paper is organized as follows: the next chapter introduces the use of Artificial Life for simulating economic processes. After that the setting and the parameters of the multi-agent system Avalanche as an agent-based value-chain coordination experiment are described. The paper concludes with an outlook to apply the behavior of that artificial economy to research organizational change.

2. GROWING THE VALUE CHAIN FROM THE BOTTOM UP

Typically, a value chain consists of several interconnected organizational units (Porter and Millar, 1985), which use raw materials as input and by adding labor and knowledge enhance the value of their manufactured product. This output is then taken as input by the next organizational unit in line until the product reaches the consumer. One can extend that value chain from the simplest raw materials (wood, oil, gas) to more complicated products (cars, houses), even if the description resembles more of a network than a linear chain. But for any particular material-product relation it should be possible to describe a linear chain, as modeled in Fig. 2 for wood and desks. Every step in that chain uses specific knowledge, capital and labor and one can easily distinct between lumberjacks, carpenters and cabinet makers in respect to their input and output goods. The organizational structure and the coordination mechanisms applied by the different professions is not addressed in this picture, as the final product is always the same, whether all workers along the chain are independent firms or part of one large company. On a continuum between market (an organizational structure using a decentralized coordination mechanism which involves trading and negotiation between firms of either size) and hierarchy (an organizational structure which is centrally coordinated) there are many distinct coordination mechanisms how agents interact and which in turn lead, if stable, to distinctive organizational structures. It should be noted that this concept abstracts from governance structures, since a legal or management perspective is not addressed. Our clipping of a real economy begins with the raw value chain which only consists of products and workers, simulated by agents - currently no management, banks or legal system is applied. Seizing an idea of Jennings et al. (1996) to „view the business process as a collection of autonomous, problems solving agents which interact when they have interdependencies“, the approach described here uses autonomous adaptive software agents to coordinate the model value chain, which is then viewed as a man-made system with a population of entities (organizational units) with local interactions (materials and information logistics). The research object of "organizational patterns" are higher-order structures in an Artificial Life ("ALife") (Langton, 1989) sense, and so the whole model world setup is considered to fall under the research context of agentbased computational economics (ACE), which is roughly characterized as the computational study of economies modeled as evolving decentralized systems of autonomous interacting agents, thus a specialization to economics of the basic Artificial Life paradigm (Tesfatsion, 1997). The initial research question is a problem of coordination: „what makes a set of economic agents

organize into an economy?“ (Lane, 1993). The different workers (ALife agents) in the value chain are seen as typical „low-level primitives“, semiautonomous agents that are driven by local interaction rules. The lumberjack does not know about the desk finally made of the wood he cuts, neither does the cabinet maker know about the particular tree. Nevertheless, all agents together produce a probably high quality desk made of high quality wood - all by local interaction, in sequence of the distinct value chain steps, guided by Adam Smith's "invisible hand". The development of higherorder organizational structures (such as markets and firms) is, following ALife concepts, now just a matter of time.

3. AVALANCHE - AN IMPLEMENTATION OF AN ECONOMIC ARTIFICIAL WORLD The multi-agent system Avalanche is a subsidiary project of the TELOS (Telematics, Coordination Mechanisms and Organisational Structures) project, which is conducted by several researchers at the Institute for Computer Science and Social Studies in Freiburg. Research topic of TELOS are the interdependencies between the use of Telematics systems in organisations, the development of the organisational structure and the applied coordination mechanisms. The impact of the use of computers, especially networks and CSCW (computer supported cooperative work) systems has been researched in the past by several projects (Englert et al., 1996; Müller et al., 1997; Schoder et al., 1997]. The technical implementation of the multi-agent system is described in more detail in Eymann et al. (1998). Summarizing, Avalanche uses publicly available Java mobile agent libraries (Lange, 1998) and adds just a few methods considered to be economically necessary like the concept of a stock capital and automated negotiation protocols (contract nets) taken from FIPA (1998). Trading places (locations) are distributed over a TCP/IP network and the numerous mobile agents move autonomously from one location to another in concurrent and parallel search for bargains. The agents differ in the materials they consume and the goods they produce. Following the model value chain depicted earlier, Avalanche "employs" three subsequent agent types, lumberjacks (trees to boards), carpenters (boards to plates) and cabinet makers (plates to tables). Trees are introduced by forester system agents, who produce with a probabilistic rate out of nothing; at the end of the chain, consumer agents buy the tables off. All agents use a heuristic negotiation strategy that depends on stochastic variables (e.g. acquisitiveness (the likelihood to change an offer price as supplier or a demand price as buyer), price-prospect (the likelihood to expect a

changing price from the supplier or buyer), satisfaction (the likelihood not to request an additional offer from a supplier) or impatience (the likelihood to change the location if not confident with the market situation)), whose values are determined at the time of the instantiation of the single agent. A key variable of any agent is the capital, which is not only a means of computing and storing "money" units when buying or selling, but also an indicator of the relative success of the single agent. An agent which is faster and/or fitter in trading as another will develop a relatively higher capital account over time. An example for the effect of the stochastic operations works as follows: the supplier agent announces the availability of finished goods on its behalf on the registration list of the current location. The current price for his products is revealed only on a local buyer's request. Since it is unlikely that the products can be sold off at once, the offer may last some time. To model economic offer behavior with respect to the self-interest of the agents, a heuristic concept of Kasbah (Chavez and Maes, 1996) is changed to follow a probabilistic decay function, checking at time intervals against the supplier's "acquisitiveness" parameter. If the check fails, the supplier will lower its offer price in the next phase, thus making a successful selling act more and more probable (regardless of the buyer's actions). If the goods can not be sold at all (e.g. if no agent of the next value chain step is alive or in reach), the offered price can even fall down to zero. At the same time, "costs of life" are constantly withdrawn from the agent's account to reflect reality's fixed costs, which in turn prevents "doing nothing" as a favorable strategy option. The use of Genetic Algorithms (GAs) in this economic simulation is motivated by the need to identify those parameter values which lead to an agent's success or demise (Holland and Miller, 1991). The success of a single agent is relatively easy indicated by a high capital account compared to other agents. The success of the population as a whole can be measured in economic (Pareto efficiency, social welfare) or technical terms (computational or communicational efficiency) (Sandholm, 1996). In Avalanche, an agent population is considered to be superior to another when the productivity per time unit is higher. The purpose of the GA is then to rule out agents with inferior parameter settings and to promote superior agents in order to achieve a nearoptimal productivity of the overall system, regardless of the initial agent population and their parameter values. Because of the dynamic and non-linear progression of a simulation run also agents can emerge and rule whose attributes are optimal in the environment given even if the initial population did not exploit that evolutionary niche.

simulation which are diverse from those currently „employed“ and working and which can grasp the evolutionary chance of changing the system in their favor.

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Figure 3: Visualization and Implementation of the Artificial World Goldberg (1989) distinguishes four steps of a genetic algorithm (evaluation, reproduction selection, recombination, mutation). In the evaluation phase each agent will be judged by a fitness score, and those agents which are eligible for reproduction are selected. The next step of recombination is usually done by taking attributes (genes) from paired agents and have the offspring agents implement a mixture of the attributes of these parent agents. On top of that, mutation changes some few parameter values at random. Because it lacks economic theory, the use of GAs in economic MAS simulations is controversial (Vriend, 1994). In contrast to living beings, economic institutions and organizational units possess a theoretically unlimited lifetime and do usually not reproduce or generate offspring in a biological sense. The economic GA used here thus abstracts from biological GAs and is implemented as follows. The fitness score uses of course the single agent’s capital. Unsuccessful agents are removed when the capital reaches zero, which is interpreted as bankruptcy. All other agents pass the evaluation and continue to live. Reproduction occurs by using a simple biological cleavage algorithm on extremely successful agents: the sudden appearance of clones can be rooted in microeconomic theory with the argumentation that obvious success of one firm in a market leads to entries of new firms who adapt to the observed successful behavior (Varian, 1993). The final two phases (recombination and mutation) will not be implemented in Avalanche; to keep things simple, genetic diversity is brought in by additionally and random attributed instantiation of agents anywhere in the model world in equidistant periods. Therefore, a certain amount of agents will always exist in the

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Figure 4: Transparency of Supply and Demand

Running the simulation, the agents will organize themselves in spatially dispersed trade networks while moving through the experimental environment. As this article describes research work in progress, the visualization shown in Fig. 3 is prototypical and does not show an actual snapshot of the environment. The lines connecting the agents show the trade relations between them. A thin line between for example a „carpenter agent“ and a „cabinet maker agent“ means a cooperational relationship between them where the cabinet maker has as its last action successfully purchased some boards from the carpenter. If its next purchasing action will again involve the same carpenter agent, the line will be drawn thicker to show the repetition. Then the repetition of once established trade interactions will be evaluated: the cooperation volatility measures the frequency of repeating an observed interaction and can be easily aggregated and displayed. The coordination volatility as the variation speed of the medium cooperation volatility of all agents can, as transition frequency between two relatively stable states, also easily be measured. Several different scenarios are likely to appear - an obvious result would be the absence of any cooperation (infinite cooperation volatility, zero coordination volatility), which means that given the initial or developed parameters any trade conducted is worse than to wait in silence. Another scenario with near-zero volatility and zero coordination volatility shows a stable state of trading where oncefound partners are never changed, regardless of new agents arriving in the environment. Changing the initial parameters (e.g. transaction costs, cooperational behavior, transparency of supply and demand, connectivity of market places) in subsequent runs should then lead to different scenarios. Some examples should be given here to show how initial parameters are ceteris paribus changed in subsequent simulation runs. An example for changing the agents parameters is a simple variation of transaction costs. Transaction costs occur with each sale and purchase, and are payable either to the trading partner or to the location the trade takes place on. The research question is simple but though not easy to answer: "would it change anything if the transaction costs are low or high?" A different example for dealing with the simulation environment itself is given by modeling transparency of supply and demand. In case of a single trading

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Figure 5: Connectivity of Trading Places location, high transparency is potentially given, since every buyer agent has the chance to get a full overview of the current offer situation. Increasing the location/agent ratio, as shown in Fig. 4 then decreases transparency while probably opening the opportunity for agents with inferior parameter settings to survive longer. The connectivity parameter of Fig. 5 is especially relevant for analyzing the „move-to-the-market“ hypothesis. By increasing the physical connections between trading it becomes less expensive for an agent to trade with an agent from a different location by physically moving and conducting trade. This should enable a tendency of a more frequent shifting of cooperation partners and thus increase the cooperation volatility.

5. CONCLUSION AND OUTLOOK Artificial life concepts maintain global coordination by modeling local interaction rules and building on competition between individuals. This basic idea is exploited here in an economic context to coordinate business processes using autonomous software agents, which cooperate and communicate using economically interpretable protocols. Starting with a random initial state of an economic agent population, the development of this population by applying local agent interaction rules and the observable coordination patterns will be investigated. The mobility of the agents allows them to roam the net in search for the most suitable offer or demand situation. Success in this task leads to a reward by allowing to reproduce the agent’s "genes" (parameter settings) using genetic algorithms, which dynamically leads in time to an agent population which is best adapted to the current supply and demand situation. Changes in this situation will be accompanied by changes in the agent population without having to interfere from the outside. The cooperational relationships between the single agents, which form trade networks over time, are then analyzed, measured and interpreted in order to answer the question if the use of computers and networks in successful existing organizations,

promising e.g. lower transaction costs and higher connectivity, leads to smaller or larger firms, e.g. if more market-like coordination or more hierarchical coordination can be proposed. Abstracting from legal viewpoints, a continuum between markets and hierarchical organizations is proposed by introducing "cooperational volatility" as a measure for the frequency of repeating an observed interaction between participants of the same value chain process, regardless of them being part of one firm or not. The sensitivity of this measure to changing simulation parameters is then explored. Expressed in terms of chaos theory (Kauffman, 1995): if there are stable attractors somewhere on the continuum between markets and hierarchies, one should be able to explore and distinguish between the trajectories leading to these attractors by changing the initial population and environment variables. Preliminary results of running the software prototype show that the agents disperse over the network while cooperating and maintaining the business process coordination. The next step will be to generate different organizational patterns by changing the parameters, and to interpret and control them in accordance with empirical data obtained from case studies of small and medium enterprises. Shortcomings of this approach are that the interpretation of the simulation results is affected by untouchable aspects of the computational implementation (because of the use of standardized mobile agent libraries and negotiation protocols) as well as the uncommon introduction of the cooperation and coordination volatility parameters. The technical openness of the application and the reduced complexity of the single agents counterbalances these shortcomings in the view of the authors, while still allowing useful research statements to be made.

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