nomics and distributed artificial intelligence to create distributed information systems, which .... Starting with the price relations wit- nessed, they create plans, ...
Eymann, Torsten/Padovan, Boris/Schoder, Detlef (2000): The Catallaxy as a new paradigm for the Design of Information Systems. In: Proceedings of the 16th IFIP World Computer Congress, Conference on Intelligent Information Processing, Beijing/ PR China, August 21-25, 2000, forthcoming
The Catallaxy as a new Paradigm for the Design of Information Systems Torsten Eymann, Boris Padovan, Detlef Schoder Albert-Ludwigs-Universität Freiburg Institut für Informatik und Gesellschaft, Telematics Dept. Friedrichstrasse 50 79098 Freiburg im Breisgau, Germany {eymann| padovan |schoder}@iig.uni-freiburg.de
Abstract The future of networking will be shaped by open, heterogeneous and complex structures, consisting of many independent information systems, which may co-operate with other systems if advantageous, but usually work in an autonomous, self-interested and not necessarily co-operative way. In this article, we propose a novel approach for information system design (named Catallactic Information Systems (CIS)), that combines efforts from the fields of economics and distributed artificial intelligence to create distributed information systems, which consist of software agents with economic reasoning and evolutionary learning capabilities. A working software prototype for supply chain coordination shows the implementation of the concept and leads to a discussion of promising application areas and known shortcomings of the approach.
Keywords Multi-Agent Systems, Agent-based Computational Economics, Supply Chain Management, Catallactic Information Systems, Catallaxy
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The future of networked information systems
The future of networking will be shaped by open, heterogeneous and complex structures, consisting of many independent information systems, resembling and evolved from today’s Internet. Among others, we envisage the evolution of the Internet into a freemarket information economy in which billions of artificial software entities exchange a rich variety of information goods and services with humans and amongst themselves. This economy can be described as an emerging "information ecosystem” (Kephart et al. 1998) that constantly scales up or down, evolves and adapts in order to best meet the changing demands of its vast and highly dynamic population of
"infohabitants" (Flores 1999). Each information system, which is designed to pursue the goals and to express the interests of its principal, appears as an infohabitant to others. It may co-operate with other systems if this is advantageous, but normally works in an autonomous, self-interested and not necessarily cooperative way. The infohabitants inevitably belong to different organizations, authorities, and cultural groups with their own kind of social behavior and cooperation protocols. The selfish pursuit of individual optimality will threaten the overall stability of the network (Kephart et al. 1998), and may finally result in a governmentless "state of nature", a "condition of war of every one against every one, in which case every one is governed by his own reason" (Hobbes 1651/1980). But can we construct a sovereign or a "common power" to prevent this from happening? In the event of a conflict amongst the participants, the information economy will lack the single centralized control institution, that can be assumed to exist in stand-alone closed information systems. So, paradoxically, while the power of the information economy largely stems from the sheer size of information and the range of activities it supports, this very size and complexity will make it impossible to harness when it comes to meeting the specific needs of particular infohabitants. The sovereign of classical information systems, the designer, has no powers that go beyond his single infohabitant, and is instead continuously forced to adapt the system to changing environmental conditions. In a world where no organisational stability can be assumed, we have to rely on "information systems (that) should be under constant development, can never be fully specified and are subject to constant adjustment and adaptation" (Truex et al. 1999). Thus, the design of such systems has inherently to embed decentralized control, openness to other systems, and continuous improvement, in order to meet the re-
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quirements of the information economy. In the following section, we will propose such an approach for information system design. Catallactic Information Systems (CIS) combine efforts from the fields of economics and distributed artificial intelligence. After that, we present the software prototype AVALANCHE, which decentrally coordinates a supply chain using software agents with economic reasoning and evolutionary learning capabilities. We conclude with an outlook to promising application areas and known shortcomings of Catallactic Information Systems.
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Economic concepts for information systems design
The proven ability of a free-market economy to adjudicate and satisfy the conflicting needs of millions of human agents recommends it as a decentralized organizational principle (Miller/Drexler 1988, Huberman 1988, Clearwater 1995, Wellman 1996). Agents may be designed to cooperate (Huberman et al. 1996) or to compete (Hogg/Huberman 1991), but so long as the aggregate evolves toward a globally defined optimum, the system as a whole is deemed successful. But in an open system, there is no global purpose being served by the collective of agents; in a sense, there is no collective. Agents' goals may be harmonious, conflicting, or unrelated, as the case may be (Rosenschein/Zlotkin 1994). One cannot prescribe a universal medium of exchange, a universal ontology of goods and services, or a universal set of agent types or algorithms. Rather, these must emerge as the system evolves (Kephart et al. 1998). The only practical way to manage such a large heterogeneous dynamic system is to make it self-organizing and self-regulating through emergent institutions. A promising concept to build the design of such systems on, is Hayek’s notion of the Catallaxy. Friedrich August von Hayek (1899-1992) got his 1974 Nobel prize in Economic Science, but his scholarly endeavours extended well beyond economics. The following synopsis about the Catallaxy is largely taken from (Hoppmann 1999) and (Hayek 1988). The term Catallaxy derives from the Greek word katallatein, which means "barter” and at the same time "to join a community”. Some of the requirements for Catallaxy to appear are (1) that agents work in their own interest to gain income, (2) that they subjectively weigh and choose preferred alternatives, and (3) that they have access to markets. A central presumption is "constitutional ignorance”, assuming that it is impossible (and incurable) to know each and every circumstance that determines the agent's action.
To discuss Catallaxy further, we have to look at the forces that drive its single elements, the economic units. Adam Smith called the emerging result of the interplay of the economic units the "invisible hand” and linked the pursuit of the own good to its service to other participants. To achieve success in that pursuit, the agents are required to estimate the particular plans, expectations and actions of others, without ever acquiring total knowledge. Catallaxy is a method to inform the individual about the knowledge of others – the trade of information bits leads to the generation of prices, which comply with the value every exchange partner assigns to the respective information, and without the reasons for their valuation becoming explicit. By orientating themselves by the price relations, the agents learn to compare and evaluate the alternatives at their disposal. The agents are motivated and directed by their income expectations. Starting with the price relations witnessed, they create plans, determine their market behavior, and revise both, according to the experiences learned from the market. The particular price relations change constantly, and these changes provide both information and motivation. Thus, income expectations and changes in the price relations are the stabilizers of the system as a whole, without which coordination of the actions of the agents would soon be discontinued. As far as the agents follow the common rules of conduct, there is spontaneous coordination of their actions. An order develops where agents are able to realize their plans more or less successfully – in a completely chaotic world it would be impossible to pursue any objective at all. Consequently the pattern that arises from coordinated behavior is denominated as the "spontaneous order” for market exchange. The peculiarities of the spontaneous order are determined by the particular plans and actions of the agents, which are a priori unknown, and impossible to forecast. Nevertheless, one can use the spontaneous order without having to know how it came into existence and what the particular results will be. All markets are connected to each other – this "interdependency of prices”, which points out that the complete set works as a system, defeats ignorance by bringing a priori unknown knowledge pieces into use, which are widely distributed among anonymous individuals. The price system is therefore a method of information about concrete circumstances, developed by the agents without their explicit intention. Like every evolution, Catallaxy develops self-organizing processes, but disallows us to forecast the development, which results from the evolutionary selection process itself.
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Catallactic Information Systems
We can now propose some basic building blocks for a novel information system concept, which formalizes the vagueness of the Catallaxy. In honor of Hayek, we will refer to systems which implement these building blocks as Catallactic Information Systems. First, the technology of autonomous agents and multi-agent systems represents a new way of analyzing, designing, and implementing complex software systems (Bradshaw 1997). A multi-agent system (MAS) can be defined as a loosely coupled network of problem solvers with local reasoning capabilities. They work together to solve problems that are beyond the individual capabilities or knowledge of each problem solver (Durfee/Lesser 1989). These problem solvers (agents) are autonomous and may be heterogeneous in nature. The characteristics of a MAS that is able to solve complex tasks, are that (1) each agent has incomplete information, or capabilities for solving the problem, thus each agent has a limited viewpoint, (2) there is no global system control, (3) data is decentralized, and (4) computation is asynchronous (Jennings et al. 1998). Second, economic reasoning implements the perception of price relations and price change signals. This includes the pursuit of an utilitarian goal and the ability to trade goods in exchange for common utility denominators, e.g. money. If an agent is to negotiate on behalf of a principal, it needs (1) a representation of the goods or services which are to be traded, (2) an understanding of what the principal wishes to achieve from the negotiation, and (3) a strategy for negotiation which is at least as effective as a qualified person in the same situation (Preist 1998). The first deals to be negotiated by automated agents will be for commodity-like goods, where the price is the only factor that is considered when trading it. The first goal of the principal will be either to buy the commodities as low, or to sell them as high as possible, but in the future, a formal representation of other factors (e.g. quality) will also be needed. . Three main approaches for negotiation strategies are currently being explored (Preist 1998): the rule based approach (Sierra et al. 1997), the game theoretic approach (Rosenschein/Zlotkin 1994, Vulkan/ Jennings 1999), and the adaptive behavior approach (Tesfatsion 1997, Cliff/Bruten 1998). The adaptive behavior approach, where simple agents adapt their strategy by observing the behavior of the marketplace and their current performance, is best suited for the concept of Catallaxy. The computational study of economies modeled as
evolving decentralized systems of autonomous interacting agents is called agent-based computational economics (ACE, Tesfatsion 1997). This research field has much substance to provide to computer scientists who want to implement economic reasoning into computer systems. Third, evolutionary learning is the key to enhance the problem-solving ability of the system at runtime, to cope with increasing complexity. This can either be an agent-based learning approach, which means that the autonomous entities in the information system can choose between, enhance or evolve new strategies. Or it can be realized as a population-based learning mechanism like an evolutionary algorithm (Smith/Taylor 1998, Holland/Miller 1991), which enhances the performance of the system as a whole by promotion of successful and relegation of unsuccessful agents. Starting with a non-optimal population where each agent represents a more or less random distribution of negotiation strategies, the population gains economic strength over time. Ruling out the unsuccessful agents and promoting the successful, clusters of successful parameter combinations appear which are effective in the dynamic environment the agents live in. Because of the dynamic and non-linear progression of a simulation run, agents can also emerge and rule whose attributes are optimal in the environment given even if the initial population did not exploit that evolutionary niche (Kauffman 1995).
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AVALANCHE – A prototypical Catallactic Information System
The multi-agent system (MAS) AVALANCHE coordinates business processes using autonomous software agents. Our clipping of a real economy begins with a supply chain of self-interested agents, representing lumberjacks, carpenters and cabinetmakers (see Fig.1). Our application scenario pictures an electronic marketplace anywhere on the Internet and real craftsmen who try to sell and buy goods by first configuring a (mobile) agent in their web browser, PDA or cell phone and then send it to the electronic marketplace to autonomously negotiate. Since we do not know enough real carpenters, lumberjacks and cabinetmakers, who can set up their software agents and determine the action strategy, we use some random variables for determining an observable complex negotiation strategy. Our technical implementation uses standardized concepts of Java 1.2 and mobile agent class libraries (ObjectSpace 1999, Lange 1998). The marketplace is a Java Virtual Machine process, which can be ad-
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real life when people individually instruct their representative trade agents. The capital account of the agent 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 have a relatively higher income and capital account. Apart from costs incurred for materials and production, the agents have to pay "taxes" for the utilization of computer resources like memory, bandwidth and processor time. An agent who does nothing will run out of money after a short time and be discarded from the system. 140
dressed and distinguished by a unique port number on a TCP/IP-network connected computer. The marketplace itself poses a sandbox environment for the agents and offers a directory service, but never actively influences or coordinates the agents’ activities. The agents communicate by bilaterally passing messages. The negotiation protocol used has been largely taken from FIPA's Agent Communication Language (FIPA 1997), implementing automated negotiation like in other projects in the research context of agentmediated electronic commerce (Chavez/Maes 1996, Preist 1998), self-interested computationally-bounded agents (Sandholm 1996, Smith 1980) and marketoriented programming (Wellman 1996). The mobility of the agents allows them to autonomously roam a network of interconnected marketplaces in search for the most suitable offer or demand situation (Eymann et al. 1998a). Now that the technology is in place, we have to describe how economic reasoning works. First of all, the utility function chosen simply tries to maximize the agent's capital. By going through the loop shown in Fig. 2., the agents try to buy materials low and sell their products high to other agents who need them as input. The negotiation among the agents can be characterized as bilateral haggling, a sequence of propose and counter-propose messages which eventually leads to a compromise price level at which goods and money are exchanged. The strategy of the agent, conceived as a nondeterministic finite state machine (Markov model), is influenced by stochastic probes against parameters like acquisitiveness (the likelihood to change an offer price as supplier or a demand price as buyer) or satisfaction (the likelihood to continue the ongoing negotiation). In all, we have 6 parameters which determine a fairly complex negotiation output and a certain heterogeneity of the agents, like we would expect it in
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Fig. 1: A model supply chain
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Fig. 2 : Price Level Development Fig.2 shows how the 4 different goods prices (wood, board, plate, table) develop over time through the mutual trade of 50 agents of each type. The horizontal axis measures the simulation time in milliseconds. The vertical axis shows the prices of the traded goods. Every dot in the graph points out a transaction, marking the time when e.g. some carpenter agent has sold a plate to a cabinetmaker agent for y-value price. The different agent types can be thought of as being inbetween two curves, buying the lower goods type and selling the higher type. Our final products (tables) are being produced with the agents decentrally solving the NP-complete problem of resource allocation in a market, thus constructing the "spontaneous order". If all agents are alike, the price levels will randomly walk around the initial start values. If we move towards a more realistic and heterogeneous setting, we can show how evolutionary learning emergently controls the environment. In Fig.2, the carpenter agents are at first able to increase their utility at the expense of the both lumberjacks and cabinetmakers, because they were initialized with a greedier negotiation strategy than all other types. The acquisitiveness parameter determines the disposition of the agent to effect a compromise; a value of 0.9 would denote a probability of 90% to stick with the price of the own last offer made. It sounds like it would be a dominat-
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ing strategy to set acquisitiveness as high as possible, and in fact this is where the starting gains of the carpenters come from in Fig.3. The range of the acquisitiveness parameter was set to 0.6 (±.4) for the carpenters and to 0.5 (±.4) for all others, leading to a unique negotiation strategy for every agent. During the course of the simulation, unsuccessful agents/strategies are relegated and successful agents/strategies are effectively doubled by the application of a distributed evolutionary algorithm (Smith/Taylor 1998). In Fig.4, every dot in the graph marks the average of all acquisitiveness values of a specific agent type, at the time of birth of a new agent. One can see that the average of the acquisitiveness parameter changes in all agent types towards some temporary equilibrium ratio. These preliminary findings already show the difference of the self-regulating CIS approach to a centralized system, where one would need to constantly monitor and control the agents to assure that no one is exploited. 0.8
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Promising application areas
Some of the application areas in which CIS-based applications are promising, and where agent-based prototypes, with and without economic reasoning and evolutionary learning, already have been developed, are highlighted below. More detail is provided in (Jennings et al. 1998, Parunak 1998). Electronic Commerce. With the advent of the Internet, it is becoming possible to trade with organizations and individuals across the world at the click of a mouse. This trade doesn’t just take place between consumers and businesses - the bulk of it is between businesses. Negotiation plays an essential part of most business-to-business transactions. Responsibility for much of this negotiation will be handed over to software agents. These agents will monitor other trade agents continuously, watching for potential opportunities. They will be able to enter into negotiation with many potential trade partners at once, reaching an
acceptable deal and setting up a contract in a matter of milliseconds (Preist 1998). Business Process Management. Company managers make informed decisions based on a combination of judgement and information from many departments. However obtaining pertinent, consistent and up-todate information across a large company is a complex and time consuming process. For this reason, organizations have sought to develop a number of IT systems to assist with various aspects of the management of their business processes. Agents who require a service from another agent enter into a negotiation for that service to obtain a mutually acceptable price, time, and degree of quality (Jennings et al. 1996). Manufacturing. Systems in this area include those for manufacturing control, configuration design of manufacturing products, collaborative design, scheduling and controlling manufacturing operations, controlling a manufacturing robot, determining production sequences for a factory, and holonic manufacturing systems (Parunak 1998, Fischer 1999). Process Control. Process control is a natural application for agents, since process controllers are themselves autonomous reactive systems. Agent-based process control systems have been written for electricity transportation management, particle accelerator control, nuclear power plants, spacecraft control, climate control and steel coil processing control. Telecommunication systems are large, distributed networks of interconnected components which need to be monitored and managed in real-time. For example, when conflicts between entities (services, nodes, components) are detected during the set up of a call, the agents negotiate with one another to resolve them so that an acceptable call configuration or quality of service is established (Albayrak/Garijo 1998). Transportation Systems. The domain of traffic and transportation management is well suited to an agentbased approach because of its geographically distributed nature. Applications are e.g. in distributed vehicle routing (Sandholm 1996), car pooling (Burmeister et al. 1997), or air traffic control (Ljunberg/Lucas 1992).
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Known shortcomings
It is desired to design mechanisms for self-interested agents such that if agents maximize their local utility, the overall system behavior will at least be acceptable (Binmore 1992). There are, however, many problems facing such a society of self-interested agents. First, agents might overuse and hence congest a shared resource, such as a communications network. This problem is called the tragedy of the commons
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(Hardin 1968). The problem of the tragedy of commons is usually solved by pricing or taxing schemes. Second, a society of self-interested computational agents can exhibit oscillatory or chaotic behavior (Huberman/Hogg 1988, Thomas/Sycara 1998). Complex behavior can be exhibited by very simple computational ecosystems, and it can emerge either as desirable or undesirable emergent behavior. Experimental results indicate that imperfect knowledge ("constitutional ignorance") suppresses oscillatory behavior at the expense of reducing performance. In addition, like in the real world, we will need norms and institutions to provide a legal and commercial framework such that global behavior emerges as desirable. The final problem with self-interested systems is the appearance of untruthful or malevolent agents. This may have a harmful effect on the whole society. Market-based modelling research includes then the implicit emergence of ”fair” prices through distributed market effects, market mechanisms that implicitly insure contractual obligations, and mechanisms that discourage "lying" (Sandholm 1996). Technical examples are the use of tamper-resistant, code-encrypted agents, the mandatory use of symmetrical and asymmetrical cryptography for communication or the authentication of agents with digital signatures or certificates.
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Conclusion and outlook
In this article we have shown how the natural connection between an economic concept and information systems technology leads to a new way of designing information systems in open, decentralized and networked application environments. To achieve this vision requires radical restructuring and insight across a range of relevant research areas, for example, life sciences, distributed systems, software engineering, computational logic, artificial intelligence and human computer interaction as well as economics, organizational theory or fundamental social science. Distributed Applications Layer
Modelling Process (bound by constitutional ignorance)
Catallactic Systems
SelfOrganisation (as result of spontaneous order)
Multi-Agent Technology Layer
Fig. 4: Catallactic Information Systems
In our view, AVALANCHE stands at the very beginning of research into Catallactic Information Systems. In Fig. 4, we have indicated how future research work can be divided in the agent technology layer and an application-specific layer. Both are linked in a feedback loop. On one hand, the technology has to constantly (and imperfectly) model an ever-changing state of the application world. On the other hand, technology’s results and the behavior of its single elements directly influence the application state by means of self-organization. The field of agent technology is a vibrant and rapidly expanding area of research and development. However, despite the obvious potential, there are a number of fundamental research and development issues which remain (Jennings et al. 1998). Our future research will address the design of control institutions for large, open, and heterogeneous agent societies. These institutions should influence the multi-agent systems, to enable them to emergently develop towards a state of desirable global behavior where security, trust and welfare is provided to all participants. This article combines efforts from both agent technology and economics, notably agent-based computational economics, to develop new technical possibilities of designing information systems. Our research and software is still in its early infancy, but we hope to be able to provide a first "proof of concept" for Catallactic Information Systems. REFERENCES 1. (Albayrak/Garijo 1998) Albayrak, S., Garijo, F.J. (Eds.): Intelligent Agents for Telecommunication Applications. LNAI 1437. Springer-Verlag: Berlin, Germany, 1998. 2. (Binmore 1992) Binmore, K.: Fun and Games: A Text on Game Theory. D. C. Heath and Company: Lexington, MA, 1992. 3. (Burmeister et al. 1997) Burmeister, B., Haddadi, A., Matylis. G: Applications of multiagent systems in traffic and transportation. IEE Transactions on Software Engineering, 144(1), pp. 51-60, February 1997. 4. (Bradshaw 1997) Bradshaw, J.M.: Software Agents. Menlo Park: MIT Press, 1997. 5.
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