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A networked expert system framework for economic policy analysis
Journal of Economic Dynamics and Control 14 (1990) 201-217. North-Holland
A NETWORKED EXPERT SYSTEM FRAMEWORK FOR ECONOMIC POLICY ANALYSIS*
Varghese S. JACOB Ohio State University, Columbw,
OH, USA
James R. MARSDEN lJniversi[v of Kentucky, Lexington, KY 40506-0034, USA
Received January 1989, final version received August 1989 Economists have traditionally undertaken mathematical analysis of policy alternatives prior to their implementation. To fully understand the potential impact of policy, market participants must understand both the microeconomic operating rules of the market and the likely policy choices of the competitors. Typically, policy choices depend upon specific quantitative measures, the specific market environment, and participant heuristics. To successfully model the market interactions and outcomes, one needs to capture not only the quantitative aspects of the policy but also necessary qualitative heuristic information. A tool which can enable such modelling is expert systems. In this paper we explore model@ the microeconomic environment as a network of expert systems. The rules of the market are modelled as one expert system. Other expert systems model quantitative and heuristic policy implementation rules of market participants.
1. Introduction The usefulness of expert systems extends far beyond the replication of existing human expert knowledge and problem-solving capability. Such systems hold the potential for analyzing important questions concerning experts operating in structured systems which possibly have never actually existed. In such cases, the analysis would involve the use of expert systems to model the counterfactual, to model the likely result of introducing mechanisms or classes of experts some of which have no counterpart in historical occurrence. Consider economic policy analysis. Economic conditions can change rapidly over time. Markets have rapidly become more international. Electronic trading mechanisms have altered the choices and narrowed the time necessary to implement a decision. If we are to successfully analyze alternative economic policies in order to choose one likely to lead to desired results, does it make sense to restrict our consideration to policies closely paralleling previous ones? *The authors are indebted to the editor, an anonymous referee, and Professor Clyde Holsapple for several helpful comments and suggestions.
0165-1889/90/$3.50~1990,
Elsevier Science Publishers B.V. (North-Holland)
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Does it make sense to model economic behavior without consideration of altered possibilities? In the sections that follow, we set forth a conceptual approach for using a network of expert systems to model and investigate economic policy questions. The individual expert systems in the network are used to model existing agents or newly determined economic agent groups (e.g., dairy farmers, oil drilling companies, NYSE brokers, or former federal government employees less than one year out of government employment). Each such expert or group of experts can be affected by economic policies (or administrative policies with economic impacts) in quite different ways. Each has differing choice sets available for their pursuit of economic goals. These choice sets are often impacted by economic or administrative policies such as dairy subsidies, oil depletion allowances, stock trading requirements (insider trading restrictions, margin requirements, etc.), or government employee lobbying restrictions. If we are to be able to study potential economic policies and analyze their likely impacts, our methodology must be capable of such efforts. We must be able to represent conditions (policies and/or experts) that may not possess satisfactory historical parallels. Our discussion is based on the assumption that each entity within the market environment is separate and modelled on an individual machine, all of which are networked together. Thus, throughout the paper, we use terminology consistent with this framework. For example, the term ‘interaction’ is used to denote communication between the various entities within the market. It should also be noted that in an actual implementation, information on all entities can be stored within a single machine as separate knowledge sources. Section 2 begins our methodological presentation by describing the problems that need to be addressed by policy analysis. Section 3 outlines the various knowledge representation schemes needed to operationalize the expert system framework. Section 4 describes the methodology we advocate: a networked expert system methodology for economic policy analysis. The latter parts of this section outline an example using our conceptual framework. Our focus here is on presenting a sound methodology for economic policy analysis. Though we suggest areas of implementation, our efforts here are directed at putting forth a conceptual framework, on laying the groundwork necessary for successful policy analysis using expert systems.
2. Economic modelling Economics is commonly defined as the ‘study of the allocation of scarce resources in an attempt to satisfy insatiable wants’. The analysis of competing (and usually conflicting) allocation policies is the continuing focus of economic research in academic as well as political circles. While some analysis is
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undertaken with a normative bent (that is, determining policies to achieve specific desired goals) and others are undertaken as objective ‘positive’ studies, the analytic procedures should be the same. In either case, the goal is to determine the impact of an economic policy choice. Economic policies are often framed as ‘macro’ in nature’, That is, the policy is viewed as having economy-wide impact. The Federal Reserve System may pursue tight money policy to ‘fight inflation’. Congress may instigate special expenditures aimed at lowering employment. Taxes may be increased or decreased in attempts to spur or contract economic activity. But, whatever the level of end intentions, a key fact remains unchanged. The impact of any economic policy is the result of micro interactions of economic agents in the context of economic allocation mechanisms. This realization helps focus what it is that we seek to do when we say we are ‘investigating or modelling economic policy making’. As we will explain in detail below, it is also the critical reason that expert system techniques hold great potential for contributing to these analyses. Economics focuses on the actions and interactions of three sets of agents: consumers (C,), producers (Pi), and government ( Gk). An ‘agent’ or element of any of these sets may be an individual or a group of individuals or a seemingly abstract entity such as a government agency. In the context of many economic policies, the distinction between producer and consumer (and even finer subgroup distinctions) can be critical. Special tax exemptions (or penalties) or subsidies may be attached to membership of a specific group or subgroup such as oil producers or dairy farmers. The impact of a policy may largely be a function of how difficult (or how easy) it is to switch group membership. For example, if special funding is provided to support economic development in inner city ‘enterprise zones’, then determining whether specific areas are or are not in this class is crucial to determining the impact of such a policy. In other contexts, the differential grouping is of little, if any, importance. In the former cases, the specification of characterizations would play a key role in determining the final implications of economic policies. In the latter cases, they would be of little importance. The basic view that underlies our research is that if economic policies are to be fully utilized in pursuit of specific goals, then we must understand and be able to measure the likely impact of these policies. Analysis of alternatiue economic policies involves comparisons of the likely impacts of the di$eent policies. But if such analysis is not performed in an interactive fashion, the analytic results are unlikely to be reliable. Economic market allocations are the result of interactions between various agents, interactions that are guided by the incentives or disincentives provided through economic policies. But can’t we simply utilize standard empirical economic analysis to address these questions? What limits our ability to employ field trials or utilize existing data on economic market activity? First, in order to determine the allocation
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impacts of economic policies, these policies must be in place, providing the requisite incentives or disincentives to economic agents. Field trials are expensive (and can be error-prone) and existing empirical data is limited to observations (and usually incomplete and somewhat flawed observations) of previously utilized policies. Further, repetitious ‘ trials’ of economic policies adds uncertainty to economic decision making as agents allocate more and more resources to attempt to determine upcoming changes. Consider, if you will, the uncertainty introduced into so many markets (such as rental real estate, tax shelters, retirement funds) by expectations surrounding the economic policies embodied in the 1986 United States federal tax (‘incentive’) changes. Agents acted and re-acted as each new rumor of a ‘solid change’ made the rounds. Corporate expansion plans were put on hold until investment-incentive changes could be determined or at least reliably estimated. The point we wish to emphasize is that the introduction of new policies as ‘ trials’ can be expensive and can introduce troubling uncertainty into markets. Alternatively, if we attempt to utilize empirical information relating to specific policies utilized, we are faced with extrapolating these empirical results to market conditions that are likely to have changed significantly or to policies which are theoretically ‘similar’. In either case, the analysis is likely to prove unsatisfactory. In one we are restricted to what can be inferred by what has been tried, while, in the other, we must toy with market conditions, introducing uncertainty that confounds our attempts to measure economic policy impacts. These form primary reasons for our proposing expert system analysis of economic markets. Other key reasons for utilizing the expert system methodology in economic analysis are the advantages offered for flexible ‘what if’ analysis and the possibility of incorporating learning mechanisms. As we will detail more fully below, interactive expert systems (including a market mechanism expert system) enable us to study the dynamics of market adjustment. Such analysis can be critical since’the impact of alternative policies is captured by more than simply the equilibrium level towards which variables move. The exact adjustment path and the speed of movement along that path can be crucial to a policy achieving its specijed goals.
Our review of these issues has led us toward the possibility of utilizing . expert systems in modelling rmcroeconomic systems. In two earlier papers [Hoffman, &trsden, Jacob, and Whinston (1986, 1988)] we utilized the offshore oil leasing market as the environment for discussing the use of rule-based expert system modelhng in economic analysis. These initial efforts focused on a single market and explored procedures for modelling the allocation within that market. Interactions were limited to competing bids and a mechanism for rewarding the highest bidder with the object in exchange for the bid value. We suggested means for comparing differing allocation mechanisms in that market
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(e.g., first-price sealed bid, second-price sealed bid, Dutch auction, and English auction). If we are to utilize expert system modelling in the wider arena of economic policy analysis, then we must expand the methodology we have previously suggested. In order to accomplish our goals, we find it helpful to utilize an interactive networking of expert systems. If expert systems are to be helpful in analyzing likely impacts of a variety of economic policies, then we must construct these systems and their interfaces so that the following conditions are satisfied: (1) Characterizations are sufficient to accurately identify each consumer, Ci, producer, Pi, and government agency, G,. (2) Economic incentive mechanisms incorporated in the modelling sufficiently parallel real world mechanisms under analysis. (3) Modelled event sequencing adequately parallels that of the economic policies and activities under study. Further, if our expert system modelling is to be viable in analyzing economic policies, then we suggest the use of laboratory experiments involving human subjects whose behavior is modelled as expert systems. By proper structuring, these experiments would incorporate human participants performing the parallel market functions to those modelled by expert systems, thus providing the ability to test the accuracy of the expert system formulations. Such experiments have been successfully used in analyzing competitive market behavior, auction market mechanisms, and stock market volatility [see, for example, Smith (1982)]. We also argue that such experimentation should follow the induced value approach of Smith’s (1976, 1982) experimental economics methodology. In such experiments, subjects are monetarily rewarded based upon their performance and the experimental outcome (usually at least partially random). When attempting to develop expert system models of market structures and interactions not currently present in actual market settings, the use of laboratory experiments is especially important, for no historical data sources are available for analyzing or tracking the accuracy of our expert system models or predictions. The next section provides the details necessary for our frame-based networked approach. This is followed by the key integrating section setting out our methodology and detailing how it might be implemented. As suggested above, the heart of our approach is the determination of macroeconomic policy implications through the linking of interactive microeconomic results. For the methodology to be successful, we must utilize a flexible approach that facilitates satisfaction of the criteria we have discussed in this section. As we
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point out in more detail below, much of the limitations and restrictiveness of previously suggested uses of expert system modelling can be overcome by altering methodology to take advantage of the flexibility made possible by an expert-system-based approach.
3. Expert systems An expert system is a computer program designed to solve problems in a particular problem domain, such that the problems solved and the solutions obtained are commensurate with those of an expert in the problem domain. There are several features of an expert system which distinguish it from traditional computer program [Harmon and King (1985)]. One of the key features which distinguishes expert systems from traditional programs is the separation of problem domain knowledge and the inference procedures which are used to manipulate the knowledge. This allows one to change the domain knowledge without having to worry about changing the inference procedures. Another feature of expert systems is the ability to integrate numeric and symbolic processing. Traditionally, expert systems have been developed primarily in symbolic processing domains such as medicine [Hudson and Estrin (1984) Patil (1981), Shortliffe (1986), Szolovits and Pauker (1978)j. However, this does not preclude integrating numeric and symbolic processing by the system if necessary. Expert systems differ from traditional simulation in several key ways. First, expert systems are typically interactive with the user, capable of responding to queries such as ‘why did you choose option A?’ or ‘why was certain information accessed?‘. In the market setting, this provides the ability to more fully analyze and better understand market results. Second, expert systems can be constructed to incorporate and utilize qualitative information. Third, expert systems are typically modular in nature, facilitating change or modification as necessary in either the knowledge base or the inference procedure. Since expert systems are designed to solve nontrivial problems at a level of proficiency comparable to that of an expert, the expert’s knowledge in solving such problems has to be captured in the system. One type of knowledge which is captured are heuristics which an expert has culled over a long period of time and utilizes in the solution of problems within the domain. One of the advantages noted above for utilizing expert systems in economic policy analysis is their ability to integrate the formal mathematical models typically used in economic analysis with heuristics experts use in analyzing the formal results of the mathematical models. The additional features of expert systems’ ability to answer ‘what if’ questions and to give a formal explanation of the rationale for recommending a particular decision enhances their position relative to more traditional modelling or simulation.
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In setting up an expert system one of the factors that must be considered is the knowledge representation scheme which will be utilized for coding the knowledge required to solve problems within a specific problem domain. Several frameworks have been proposed for knowledge representation [Barr and Feigenbaum (1982)]. In this paper we will be utilizing an integration of three different methods: frames, scripts, and rules. In the exposition that follows, we utilize a purely deterministic representation. This, of course, is not a requirement of these methods and is used here only as a means to simplify the exposition. 3. I. Frames Frames have several features that make their use as a knowledge representation scheme for the economic policy analysis problem attractive. Frames are a template for holding related knowledge about a particular subject [RauchHindin (1986)]. Typically, the name of the subject is used to name the frame. Information associated with the subject is stored in ‘slots’. Several expert system shells have been developed using frames as the knowledge representation scheme [see Fikes and Kehler (1985) for a detailed discussion on KEE and Waterman (1986) for a list of such shells]. Frames can be organized into taxonomies based on the concept of classes. Within each class, one can describe subclasses or specializations of the more generic class. Each individual within the class as well as the class itself is represented by a frame. The frames are generally organized in an hierarchy with the generic frames at the top of the hierarchy. This allows the lower-level frames to inherit generic properties of the class from the higher-level frames. In addition, they could have their own specialized properties. One can also specify the properties of the slots which would be stored in ‘subslots’ associated with each of the specified slots. Subslots are also referred to as ‘facets’. For example, one can specify procedures which describe how a value for a slot can be computed or what is to be done if a particular slot is filled. In our application, information about the government, consumers, and industries can be organized within frames. However, this information alone will not suffice to model the public policy decisions. In addition to information about objects, one has to also model the implications of implementing a policy or, in other words, the events which would occur given certain conditions are satisfied. This information is stored in the form of scripts. 3.2. Scripts A script describes a typical sequence of events in a particular context. A script, like a frame, consists of a set of slots [Rich (1983)]. The slots in a script
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contain
information
relating
to the following:
(a) conditions that must be satisfied before the events described in the script can occur (entry conditions in figs. 3 and 4) (b) conditions that will in general be true after the events described in the script have occurred (result conditions in figs. 3 and 4), (c) the objects, including people, that are involved in the events (‘props’ for inanimate objects and ‘roles’ for animate objects in figs. 3 and 4), (d) the specific variation on a more general pattern that is represented by the script (‘tracks’ in figs. 3 and 4) (e) the actual sequence of events that will occur (‘scenes’ in figs. 3 and 4). Utilizing scripts within the knowledge representation scheme allows one to represent the sequence of events which occur as a result of the causal relationships between events. The entry conditions specify the set of conditions which have to be satisfied before the first events in the script occur, the results of which trigger other events within the script. The end of the chain is the set of results which will occur as a result of the events occurring. These results in turn may trigger other events, i.e., scripts. Scripts have primarily been utilized for understanding stories [Rauch-Hindin (1986)j. One of the advantages of scripts is that once a script is known to be appropriate for a given situation, it can be used to predict events that are not explicitly mentioned in the text. For example, if it is stated that consumer C, purchased product A from producer P, for $5000, then, even though it is not explicitly mentioned in the text that consumer C, has $5000 less than before, it can be inferred once the script which matches the situation is instantiated. Scripts are also useful in indicating how events are related to each other. Within the context of our application, the scripts can be utilized to represent the sequence of events that occur as a result of implementing a specific policy. Thus, one can easily model the chain of events that occur and the final result of the chain as a result of implementing a policy. Examples of scripts are presented in section 4.
3.3. Rules Now that we have a mechanism for representing the objects and events, we need a framework for representing market rules. Clearly any policy which is implemented has to function within the operating rules of the market, with the government having the power to change the rules of the market. The market rules can therefore be operationalized within the system as rules. A collection of rules describing a particular market situation can then be organized within frames.
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In addition to market rules, any other expertise which has to be in the situation-action format can be represented as rules. For example, a consumer may have two different heuristics which he/she might use based on the perception of how the market is going to behave. The consumer’s faith in these heuristics could also be specified within the rules as certainty factors (CF). Thus, for example, an individual consumer may have a heuristic which specifies the following two rules: Rule 1: IF certainty of producer X going out of business is < 20% AND producer X has the lowest price, THEN producer X will not go out of business (CF 85). Rule 2: IF certain@ of producer X going out of business is < 20 X AND producer X has highest price, THEN producer X will go out of business (CF 60).
The modelling of economic policy analysis is complex enough to require the utilization of several types of knowledge. The integration of frames, scripts, and rules within the system can provide the necessary flexibility to model the market and the policies which will influence the market.
3.4. Networked expert systems for economic policy analysis
In order to completely analyze an economic policy, one has to consider the influence of the government agencies, the producers, and the consumers. Before implementing a policy, a governmental agency would be interested in trying to determine how the consumer and the producer would react to the new policy. The producer on the other hand would be interested in determining how its competitors, the governmental agencies, and the consumers react to its policy. Clearly, the volume of knowledge which will have to be stored for a realistic examination of a policy would be extremely large and cannot be practically stored in a single expert system. Even if storage were practical, the time taken to search such a large knowledge set would be unrealistic. Therefore, one needs a networked expert system, with each governmental agency, producer, and consumer modelled within a specific system, all of which either interact through the processor, which is designated as the market, or directly interact between each other, depending on the situation. Fig. 1 illustrates the network. Ideally one would expect the interactions between the various entities to take place through the market. These links are specified through the solid lines in fig. 1. However, if it is mutually advantageous, there could be interactions between the various entities which bypass the market. These interaction links are indicated by the broken lines in fig. 1.
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expert sysiems
I-GO-------
\\------Fig. 1. Possible interactions
between
the entities in a market
In setting up such a framework one has to keep in mind that it would not be practical to include all the producers and all the possible consumers. From the perspective of a given producer for implementation purposes, one should therefore include all the producers who are currently viewed as competitors, the producers who are suppliers, and a prototype of a producer who could possibly enter the market and compete effectively. It would be impossible to effectively generate all possible categories of consumers. However, one could create profiles of consumer behavior, in certain income ranges along with certain attributes, such as consumers who tend to save or are risk-averse. Given the above characteristics of the network, one can effectively simulate a market and the implications of implementing a specific policy within it, the results of which would form the basis for actual implementation of the policy in the field. 4. Implementation
issues
In this section we will consider some of the implementation issues pertaining to knowledge possessed by each node and the interaction between the nodes. 4.1. Knowledge In setting up a framework such as that proposed in the previous section, there are two possibilities: 1) the interaction between Ci, Pi, and G, takes place only through the expert system marked E, which is the market, and
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2)there is direct interaction between C,, i = 1,. . . , n, or Pi, j = 1,. . . , m, or between Ci and Pi,leading to issues such as collusion. For the purposes of this paper we focus only on the first issue. Let us first consider the knowledge which has to be incorporated in each of the nodes. The knowledge pertaining to the consumers can be broken down into five classes: 1) income and investments and perceived changes in income flow or investments (e.g., salary increases), 2) consumers’ utility functions and knowledge of its utilization, 3) consumers’ heuristics about the behavior of the economy and how to take advantage of it, 4) consumers’ heuristics about the behavior of the producers and other consumers, and 5) the knowledge about the laws goveming consumer behavior. Knowledge relating to each of the classes can be encoded in frames. For example, the income class would have a collection of frames possibly linked together. As shown in fig. 2, the topmost frame in the slot could contain slots specifying the various sources of income. These slots could be pointers to other frames in which information about this particular income could be specified. For example, the salary information which is specified in the salary frame indicates two types of income, one from a regular job and the other from consulting. Subslots (or facets) associated with each slot give more information about the slot. Here, income from the jobs ($30,000) is specified in the subslot called ‘amount’. Similarly, the income from consulting is stated in its associated subslot. As shown in fig. 2, one could organize the frames in a hierarchy, with more detailed information being obtained as one goes down the hierarchy. For example, the stock frame specifies the companies in which the consumer holds stock, while information on each such company is stored in the next level frame set. An additional feature to note is that procedures can be attached to subslots. Such procedures can be utilized to compute or change values associated with a slot. In fig. 2, the subslots ‘if needed’ specify procedures which are executed when the slot values need to be determined. Though absent for simplicity, appropriate information would normally be present in the frames labeled ‘bond’, ‘interest’, and ‘company Y’. The knowledge the producers possess can also be categorized into five classes: 1) product production knowledge, 2) product cost and marketing knowledge (including knowledge relating to issues such as profit maximization), 3) heuristics about consumer and competitor behavior, 4) governmental rules and regulations and how they affect the production and selling of the products, and 5) cash flow and investment knowledge. Governmental agencies perform two functions. They set up the rules and regulations or specify policies which determine the interaction in the market place. Secondly, they monitor the behavior of the consumers and the producers in the marketplace to ensure that they follow the rules and regulations which have been specified by the agencies. The knowledge that each govemmental agency has is the current policy information within its own jurisdiction
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Fig. 2. An example of a hierarchy of frames
and knowledge allowing it to recognize that a particular consumer or producer is in violation of the policy. Additional features of the governmental agency expert systems are the abilities to modify the policies and to communicate them to the market. Thus, one can perceive the users of this network-based approach as essentially being associated with the governmental agency expert system. The user would therefore specify a new policy and communicate it to the market and then observe the behavior of the consumers and producers after the policy went into effect. This behavior can then be compared with prior behavior or other behavior as a result of changing the policy.
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The expert system which is modelled as the market does the broadcasting of information to the consumers and producers. Further it communicates the behavior information to the appropriate governmental agency. The knowledge possessed by this system includes 1) identification of consumer and producers and 2) the ability to respond to their messages (i.e., it possesses scripts which specify conditions under which the interaction between consumer and producer can take place and the results of the interaction are then broadcast to the appropriate consumers, producers, and governmental agencies). Scripts are illustrated in the next section when we consider an example of a market. 4.2. A networked expert systems example As described above, our network consists of three types of expert systems representing Ci, Pi, and G,. For illustrative purposes, suppose that two government agencies (one federal and one state) are potential regulators of the market for an agricultural product, corn. The federal agency, G,, has been charged with allocating up to $lO,OOO,OOO in price guarantees (potentially in the form of subsidies) to eligible growers of corn in a specified state. The individual state agency, G,, actually makes the allocation decisions but must conform to a set of specific rules set by the federal agency. Only farmers, P 1,. . . , P,, that meet specified acreage, crop history, and farm-derived income requirements are eligible for the program. Consumers, C,, . . . , C,,,, can, under a 3/4 majority, vote the program out. More importantly, they can alter their demand decisions concerning corn or related products (substitutes or complements). In order for a producer to participate in the subsidized market he/she has to be determined to be eligible. Thus, one script (fig. 3) would involve the qualification of a farmer as ‘eligible’ to participate in the program and thus determine a part of the communication that can take place across the network. This might entail the checking of the slots in the individual’s ‘land frame’ to insure, for example, that he/she owns at least 100 acres and has planted and harvested at least 50 acres of corn the last five years. Additionally, the ‘income frame’ will be checked to make sure that the individual obtains at least 75% of gross income from specified farm-related activities. Once it is determined that a farmer is eligible, the relevant communication links are made open but are not necessarily utilized. Farmers who do not qualify (or choose not to apply) for crop price guarantees may still produce and sell the crop. Their market differs from the price guarantee or subsidized market in that the price they receive is subject to the choice made by the consumers. The farmers participating in the subsidized markets are guaranteed a price which does not vary. Fig. 4 illustrates a script for the subsidized market. If the actual market equilibrium price falls short of the guaranteed price, the net subsidy is positive. If the
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: Eligibility : Corn Farmer : Land, Corn, Farm, income : Farmer, Government Agency
Entry Conditions : Fanner has more than 100 acres of land Farmer has harvested at least 50 acres in 5 years Farm related gross income is greater than 75% Result Conditions : Farmer is eligible for subsidy Farmer informed about subsidy restrictions if he/she XCePtS 1 Scene 1 :Application Farmer requests participation Farmer makes available relevant information Farmer waits for information to be processed Scene 2 :Evaluation Government agency evaluates provided iniormation Query farmer for further inlormation and resolve discrepancies in provided information Scene 3 :Eligibility Farmer is deemed eligible Information on restrictions on behavior is provided if substdy is accepted Ineligible Path : Farmer is deemed ineligible Fig. 3. Eligibility
script for corn farmers.
market equilibrium price exceeds the subsidized price, then the net subsidy is negative, with the government agency capturing the monetary gains. Consumers form the final group of players in our network of expert systems. With a knowledge base incorporating data such as the prices of other commodities, weather and harvest forecasts, individual income or budget constraints, and preference orderings, the consumers put demand information into the network. For example, consumers may react to producer-issued offers to sell or consumers may directly issue offers to buy. Allocation may occur through bidding mechanisms or through repeated posted offers. Individual consumers control their demand for end products, including corn, subject to their individual budget constraints. All participants are potential consumers with the government agencies possibly acting as buyers for later resale in order to minimize the cost of their programs. As the number of market activity possibilities (e.g., allocation mechanisms, buyer and seller activities such as futures contracts, option contracts, etc.) expands, this increases the number of scripts required to fully detail the expert system network. Within the networked system, we have the following steps taking place (with the understanding that steps marked by an asterisk can occur at any point
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:comMsrket :Suba&adcommatkat :CWmonaY : Farmers, Consumars,GovernmentAgancy
Entry Conditions : Producer has corn to sell Producer has been cla.ssiiiadas eligiila subsidiiad prkx is known Result condltlon8 : Producer has lass corn to sell Consumer has corn Producer has more money Consumer has lass money Scene 1 :Availablllty Producer spa&s corn availability Subsiiad price is broadcast Consumer spadfias corn amount naadad Consumer spacifk p&a willing to pay Scene2 :Tranaactlon Ifmumar price is equal to or greater than subsidy prim than transaction mada No tnnsactlon Path : Consumer informed prim too tow - no transaubn Scene3 :NoBuyem Noconsumer willing to pay subsidii prica Governmarl agancy buys at spscibd price Fig. 4. Subsidized corn market script
subsequent period): (1)
to their initial occurrence but prior to the end of the market
government agencies initialize (set) conditions for market period - these include conditions for farmer certification and subsidy price to be offered (possibly user input); (2) farmers meeting conditions choose to opt for program price or participate in free market (farmers use knowledge about their land, income, etc. as well as heuristics about the economy, weather, etc. to make this decision); (3) farmer yields determined, i.e., corn crop realized for participating and nonparticipating producers (this would be based on acreage and randomly assigned disasters); (4)* consumer and/or producer offers entered into market (offers would again be based on knowledge possessed by the participants); (5)* consumer and/or producer responses determining sales quantities and safes prices (responses based on knowledge about the domain); (6)* transference of funds as offers accepted - credit and debit update occurring each time offer accepted (script possessed by market specifies the procedure for transference of funds);
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(7)* revised or updated offers entered into the network followed by responses (scripts possessed by participants specify the sequence of offers to be made); (8) final market structure determination - either ‘clock’ on offers expires or no offers occur for specified amount of time. Thus, by specifying the appropriate knowledge within the expert systems one can observe the interactions which occur when a policy is implemented within the market.
5. Conclusions In this paper we have shown the possible use of expert systems as a tool for economic policy analysis. In order to model the interactions, we have proposed a networked expert system framework. The knowledge required to operationalize the framework would be stored at the various nodes using three knowledge representation schemes: rules, frames, and scripts. Although the current paper focuses on illustrating the interactions which occur within the market, the framework can easily be modified to take into account interactions between the various entities that could occur outside of the market. This would imply that, in addition to domain knowledge, one would have to incorporate knowledge about whom to communicate with for a specific purpose. In a networked-knowledge-based system, this knowledge has been characterized as ‘peripheral knowledge’ by Jacob and Pirkul (1988). Enhancing the knowledge at each node with peripheral knowledge will allow one to study the market impact of interactions taking place both inside and outside the market, thus expanding the potential usefulness of expert system modelling in economic analysis.
References Barr, A. and E.A. Feigenbaum, 1982, The handbook of artificial intelligence. Vol. II (William Kauffman, Los Altos, CA). Fikes, R. and T. Kehler, 1985, The role of frame-based representation in reasoning, Communications of the ACM 28, 906-920. Harmon, H. and D. King. 1985, Expert systems: Artificial intelligence in business (Wiley, New York, NY). Hoffman, E.. V. Jacob, J. Marsden, and A. Whinston, 1988, Expert systems and economic policy analysis: An application to OCS auction leasing, Computer Science in Economics and Management 1, 113-135. Hoffman, E., V. Jacob, J. Marsden, and A. Whinston, 1986, Artificial intelligence in economics - Expert systems modelling of microeconomic systems, in: L.F. Pau, ed., Artificial intelligence in economics and management (North-Holland, Amsterdam). Hudson, D.L. and T. Estrin, 1984, EMERGE, a data-driven medical decision making aid, IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 87-89.
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Jacob, VS. and H. Pirkul, 1988, A framework for networked knowledge based systems, IEEE Transactions on Systems, Man and Cybernetics, forthcoming. Patil, R.S. et al., 1981, Casual understanding of patient-illness in medical diagnosis, Proceedings of the seventh international joint conference on AI, 893-899. Rauch-Hindin, N.B., 1986, Artificial intelligence in business, science and industry, Vol. I (Prentice-Hall, Englewood Cliffs, NJ). Rich, E., 1983, Artificial intelligence (McGraw I-U, New York, NY). Shortliffe, E.H., 1976, Computer-based medical consultations: MYCIN (Elsevier, New York, NY). Sxolovits. P. and S.G. Pauker, 1978, Categorical and probabilistic reasoning in medical diagnosis, Artificial Intelligence 11,115-166. Waterman, D.A., 1986, A guide to expert systems (Addison-Wesley, Waltham, MA).
A NETWORKED EXPERT SYSTEM FRAMEWORK FOR ECONOMIC POLICY ANALYSIS*
Varghese S. JACOB Ohio State University, Columbw,
OH, USA
James R. MARSDEN lJniversi[v of Kentucky, Lexington, KY 40506-0034, USA
Received January 1989, final version received August 1989 Economists have traditionally undertaken mathematical analysis of policy alternatives prior to their implementation. To fully understand the potential impact of policy, market participants must understand both the microeconomic operating rules of the market and the likely policy choices of the competitors. Typically, policy choices depend upon specific quantitative measures, the specific market environment, and participant heuristics. To successfully model the market interactions and outcomes, one needs to capture not only the quantitative aspects of the policy but also necessary qualitative heuristic information. A tool which can enable such modelling is expert systems. In this paper we explore model@ the microeconomic environment as a network of expert systems. The rules of the market are modelled as one expert system. Other expert systems model quantitative and heuristic policy implementation rules of market participants.
1. Introduction The usefulness of expert systems extends far beyond the replication of existing human expert knowledge and problem-solving capability. Such systems hold the potential for analyzing important questions concerning experts operating in structured systems which possibly have never actually existed. In such cases, the analysis would involve the use of expert systems to model the counterfactual, to model the likely result of introducing mechanisms or classes of experts some of which have no counterpart in historical occurrence. Consider economic policy analysis. Economic conditions can change rapidly over time. Markets have rapidly become more international. Electronic trading mechanisms have altered the choices and narrowed the time necessary to implement a decision. If we are to successfully analyze alternative economic policies in order to choose one likely to lead to desired results, does it make sense to restrict our consideration to policies closely paralleling previous ones? *The authors are indebted to the editor, an anonymous referee, and Professor Clyde Holsapple for several helpful comments and suggestions.
0165-1889/90/$3.50~1990,
Elsevier Science Publishers B.V. (North-Holland)
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Does it make sense to model economic behavior without consideration of altered possibilities? In the sections that follow, we set forth a conceptual approach for using a network of expert systems to model and investigate economic policy questions. The individual expert systems in the network are used to model existing agents or newly determined economic agent groups (e.g., dairy farmers, oil drilling companies, NYSE brokers, or former federal government employees less than one year out of government employment). Each such expert or group of experts can be affected by economic policies (or administrative policies with economic impacts) in quite different ways. Each has differing choice sets available for their pursuit of economic goals. These choice sets are often impacted by economic or administrative policies such as dairy subsidies, oil depletion allowances, stock trading requirements (insider trading restrictions, margin requirements, etc.), or government employee lobbying restrictions. If we are to be able to study potential economic policies and analyze their likely impacts, our methodology must be capable of such efforts. We must be able to represent conditions (policies and/or experts) that may not possess satisfactory historical parallels. Our discussion is based on the assumption that each entity within the market environment is separate and modelled on an individual machine, all of which are networked together. Thus, throughout the paper, we use terminology consistent with this framework. For example, the term ‘interaction’ is used to denote communication between the various entities within the market. It should also be noted that in an actual implementation, information on all entities can be stored within a single machine as separate knowledge sources. Section 2 begins our methodological presentation by describing the problems that need to be addressed by policy analysis. Section 3 outlines the various knowledge representation schemes needed to operationalize the expert system framework. Section 4 describes the methodology we advocate: a networked expert system methodology for economic policy analysis. The latter parts of this section outline an example using our conceptual framework. Our focus here is on presenting a sound methodology for economic policy analysis. Though we suggest areas of implementation, our efforts here are directed at putting forth a conceptual framework, on laying the groundwork necessary for successful policy analysis using expert systems.
2. Economic modelling Economics is commonly defined as the ‘study of the allocation of scarce resources in an attempt to satisfy insatiable wants’. The analysis of competing (and usually conflicting) allocation policies is the continuing focus of economic research in academic as well as political circles. While some analysis is
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undertaken with a normative bent (that is, determining policies to achieve specific desired goals) and others are undertaken as objective ‘positive’ studies, the analytic procedures should be the same. In either case, the goal is to determine the impact of an economic policy choice. Economic policies are often framed as ‘macro’ in nature’, That is, the policy is viewed as having economy-wide impact. The Federal Reserve System may pursue tight money policy to ‘fight inflation’. Congress may instigate special expenditures aimed at lowering employment. Taxes may be increased or decreased in attempts to spur or contract economic activity. But, whatever the level of end intentions, a key fact remains unchanged. The impact of any economic policy is the result of micro interactions of economic agents in the context of economic allocation mechanisms. This realization helps focus what it is that we seek to do when we say we are ‘investigating or modelling economic policy making’. As we will explain in detail below, it is also the critical reason that expert system techniques hold great potential for contributing to these analyses. Economics focuses on the actions and interactions of three sets of agents: consumers (C,), producers (Pi), and government ( Gk). An ‘agent’ or element of any of these sets may be an individual or a group of individuals or a seemingly abstract entity such as a government agency. In the context of many economic policies, the distinction between producer and consumer (and even finer subgroup distinctions) can be critical. Special tax exemptions (or penalties) or subsidies may be attached to membership of a specific group or subgroup such as oil producers or dairy farmers. The impact of a policy may largely be a function of how difficult (or how easy) it is to switch group membership. For example, if special funding is provided to support economic development in inner city ‘enterprise zones’, then determining whether specific areas are or are not in this class is crucial to determining the impact of such a policy. In other contexts, the differential grouping is of little, if any, importance. In the former cases, the specification of characterizations would play a key role in determining the final implications of economic policies. In the latter cases, they would be of little importance. The basic view that underlies our research is that if economic policies are to be fully utilized in pursuit of specific goals, then we must understand and be able to measure the likely impact of these policies. Analysis of alternatiue economic policies involves comparisons of the likely impacts of the di$eent policies. But if such analysis is not performed in an interactive fashion, the analytic results are unlikely to be reliable. Economic market allocations are the result of interactions between various agents, interactions that are guided by the incentives or disincentives provided through economic policies. But can’t we simply utilize standard empirical economic analysis to address these questions? What limits our ability to employ field trials or utilize existing data on economic market activity? First, in order to determine the allocation
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impacts of economic policies, these policies must be in place, providing the requisite incentives or disincentives to economic agents. Field trials are expensive (and can be error-prone) and existing empirical data is limited to observations (and usually incomplete and somewhat flawed observations) of previously utilized policies. Further, repetitious ‘ trials’ of economic policies adds uncertainty to economic decision making as agents allocate more and more resources to attempt to determine upcoming changes. Consider, if you will, the uncertainty introduced into so many markets (such as rental real estate, tax shelters, retirement funds) by expectations surrounding the economic policies embodied in the 1986 United States federal tax (‘incentive’) changes. Agents acted and re-acted as each new rumor of a ‘solid change’ made the rounds. Corporate expansion plans were put on hold until investment-incentive changes could be determined or at least reliably estimated. The point we wish to emphasize is that the introduction of new policies as ‘ trials’ can be expensive and can introduce troubling uncertainty into markets. Alternatively, if we attempt to utilize empirical information relating to specific policies utilized, we are faced with extrapolating these empirical results to market conditions that are likely to have changed significantly or to policies which are theoretically ‘similar’. In either case, the analysis is likely to prove unsatisfactory. In one we are restricted to what can be inferred by what has been tried, while, in the other, we must toy with market conditions, introducing uncertainty that confounds our attempts to measure economic policy impacts. These form primary reasons for our proposing expert system analysis of economic markets. Other key reasons for utilizing the expert system methodology in economic analysis are the advantages offered for flexible ‘what if’ analysis and the possibility of incorporating learning mechanisms. As we will detail more fully below, interactive expert systems (including a market mechanism expert system) enable us to study the dynamics of market adjustment. Such analysis can be critical since’the impact of alternative policies is captured by more than simply the equilibrium level towards which variables move. The exact adjustment path and the speed of movement along that path can be crucial to a policy achieving its specijed goals.
Our review of these issues has led us toward the possibility of utilizing . expert systems in modelling rmcroeconomic systems. In two earlier papers [Hoffman, &trsden, Jacob, and Whinston (1986, 1988)] we utilized the offshore oil leasing market as the environment for discussing the use of rule-based expert system modelhng in economic analysis. These initial efforts focused on a single market and explored procedures for modelling the allocation within that market. Interactions were limited to competing bids and a mechanism for rewarding the highest bidder with the object in exchange for the bid value. We suggested means for comparing differing allocation mechanisms in that market
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(e.g., first-price sealed bid, second-price sealed bid, Dutch auction, and English auction). If we are to utilize expert system modelling in the wider arena of economic policy analysis, then we must expand the methodology we have previously suggested. In order to accomplish our goals, we find it helpful to utilize an interactive networking of expert systems. If expert systems are to be helpful in analyzing likely impacts of a variety of economic policies, then we must construct these systems and their interfaces so that the following conditions are satisfied: (1) Characterizations are sufficient to accurately identify each consumer, Ci, producer, Pi, and government agency, G,. (2) Economic incentive mechanisms incorporated in the modelling sufficiently parallel real world mechanisms under analysis. (3) Modelled event sequencing adequately parallels that of the economic policies and activities under study. Further, if our expert system modelling is to be viable in analyzing economic policies, then we suggest the use of laboratory experiments involving human subjects whose behavior is modelled as expert systems. By proper structuring, these experiments would incorporate human participants performing the parallel market functions to those modelled by expert systems, thus providing the ability to test the accuracy of the expert system formulations. Such experiments have been successfully used in analyzing competitive market behavior, auction market mechanisms, and stock market volatility [see, for example, Smith (1982)]. We also argue that such experimentation should follow the induced value approach of Smith’s (1976, 1982) experimental economics methodology. In such experiments, subjects are monetarily rewarded based upon their performance and the experimental outcome (usually at least partially random). When attempting to develop expert system models of market structures and interactions not currently present in actual market settings, the use of laboratory experiments is especially important, for no historical data sources are available for analyzing or tracking the accuracy of our expert system models or predictions. The next section provides the details necessary for our frame-based networked approach. This is followed by the key integrating section setting out our methodology and detailing how it might be implemented. As suggested above, the heart of our approach is the determination of macroeconomic policy implications through the linking of interactive microeconomic results. For the methodology to be successful, we must utilize a flexible approach that facilitates satisfaction of the criteria we have discussed in this section. As we
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point out in more detail below, much of the limitations and restrictiveness of previously suggested uses of expert system modelling can be overcome by altering methodology to take advantage of the flexibility made possible by an expert-system-based approach.
3. Expert systems An expert system is a computer program designed to solve problems in a particular problem domain, such that the problems solved and the solutions obtained are commensurate with those of an expert in the problem domain. There are several features of an expert system which distinguish it from traditional computer program [Harmon and King (1985)]. One of the key features which distinguishes expert systems from traditional programs is the separation of problem domain knowledge and the inference procedures which are used to manipulate the knowledge. This allows one to change the domain knowledge without having to worry about changing the inference procedures. Another feature of expert systems is the ability to integrate numeric and symbolic processing. Traditionally, expert systems have been developed primarily in symbolic processing domains such as medicine [Hudson and Estrin (1984) Patil (1981), Shortliffe (1986), Szolovits and Pauker (1978)j. However, this does not preclude integrating numeric and symbolic processing by the system if necessary. Expert systems differ from traditional simulation in several key ways. First, expert systems are typically interactive with the user, capable of responding to queries such as ‘why did you choose option A?’ or ‘why was certain information accessed?‘. In the market setting, this provides the ability to more fully analyze and better understand market results. Second, expert systems can be constructed to incorporate and utilize qualitative information. Third, expert systems are typically modular in nature, facilitating change or modification as necessary in either the knowledge base or the inference procedure. Since expert systems are designed to solve nontrivial problems at a level of proficiency comparable to that of an expert, the expert’s knowledge in solving such problems has to be captured in the system. One type of knowledge which is captured are heuristics which an expert has culled over a long period of time and utilizes in the solution of problems within the domain. One of the advantages noted above for utilizing expert systems in economic policy analysis is their ability to integrate the formal mathematical models typically used in economic analysis with heuristics experts use in analyzing the formal results of the mathematical models. The additional features of expert systems’ ability to answer ‘what if’ questions and to give a formal explanation of the rationale for recommending a particular decision enhances their position relative to more traditional modelling or simulation.
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In setting up an expert system one of the factors that must be considered is the knowledge representation scheme which will be utilized for coding the knowledge required to solve problems within a specific problem domain. Several frameworks have been proposed for knowledge representation [Barr and Feigenbaum (1982)]. In this paper we will be utilizing an integration of three different methods: frames, scripts, and rules. In the exposition that follows, we utilize a purely deterministic representation. This, of course, is not a requirement of these methods and is used here only as a means to simplify the exposition. 3. I. Frames Frames have several features that make their use as a knowledge representation scheme for the economic policy analysis problem attractive. Frames are a template for holding related knowledge about a particular subject [RauchHindin (1986)]. Typically, the name of the subject is used to name the frame. Information associated with the subject is stored in ‘slots’. Several expert system shells have been developed using frames as the knowledge representation scheme [see Fikes and Kehler (1985) for a detailed discussion on KEE and Waterman (1986) for a list of such shells]. Frames can be organized into taxonomies based on the concept of classes. Within each class, one can describe subclasses or specializations of the more generic class. Each individual within the class as well as the class itself is represented by a frame. The frames are generally organized in an hierarchy with the generic frames at the top of the hierarchy. This allows the lower-level frames to inherit generic properties of the class from the higher-level frames. In addition, they could have their own specialized properties. One can also specify the properties of the slots which would be stored in ‘subslots’ associated with each of the specified slots. Subslots are also referred to as ‘facets’. For example, one can specify procedures which describe how a value for a slot can be computed or what is to be done if a particular slot is filled. In our application, information about the government, consumers, and industries can be organized within frames. However, this information alone will not suffice to model the public policy decisions. In addition to information about objects, one has to also model the implications of implementing a policy or, in other words, the events which would occur given certain conditions are satisfied. This information is stored in the form of scripts. 3.2. Scripts A script describes a typical sequence of events in a particular context. A script, like a frame, consists of a set of slots [Rich (1983)]. The slots in a script
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contain
information
relating
to the following:
(a) conditions that must be satisfied before the events described in the script can occur (entry conditions in figs. 3 and 4) (b) conditions that will in general be true after the events described in the script have occurred (result conditions in figs. 3 and 4), (c) the objects, including people, that are involved in the events (‘props’ for inanimate objects and ‘roles’ for animate objects in figs. 3 and 4), (d) the specific variation on a more general pattern that is represented by the script (‘tracks’ in figs. 3 and 4) (e) the actual sequence of events that will occur (‘scenes’ in figs. 3 and 4). Utilizing scripts within the knowledge representation scheme allows one to represent the sequence of events which occur as a result of the causal relationships between events. The entry conditions specify the set of conditions which have to be satisfied before the first events in the script occur, the results of which trigger other events within the script. The end of the chain is the set of results which will occur as a result of the events occurring. These results in turn may trigger other events, i.e., scripts. Scripts have primarily been utilized for understanding stories [Rauch-Hindin (1986)j. One of the advantages of scripts is that once a script is known to be appropriate for a given situation, it can be used to predict events that are not explicitly mentioned in the text. For example, if it is stated that consumer C, purchased product A from producer P, for $5000, then, even though it is not explicitly mentioned in the text that consumer C, has $5000 less than before, it can be inferred once the script which matches the situation is instantiated. Scripts are also useful in indicating how events are related to each other. Within the context of our application, the scripts can be utilized to represent the sequence of events that occur as a result of implementing a specific policy. Thus, one can easily model the chain of events that occur and the final result of the chain as a result of implementing a policy. Examples of scripts are presented in section 4.
3.3. Rules Now that we have a mechanism for representing the objects and events, we need a framework for representing market rules. Clearly any policy which is implemented has to function within the operating rules of the market, with the government having the power to change the rules of the market. The market rules can therefore be operationalized within the system as rules. A collection of rules describing a particular market situation can then be organized within frames.
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In addition to market rules, any other expertise which has to be in the situation-action format can be represented as rules. For example, a consumer may have two different heuristics which he/she might use based on the perception of how the market is going to behave. The consumer’s faith in these heuristics could also be specified within the rules as certainty factors (CF). Thus, for example, an individual consumer may have a heuristic which specifies the following two rules: Rule 1: IF certainty of producer X going out of business is < 20% AND producer X has the lowest price, THEN producer X will not go out of business (CF 85). Rule 2: IF certain@ of producer X going out of business is < 20 X AND producer X has highest price, THEN producer X will go out of business (CF 60).
The modelling of economic policy analysis is complex enough to require the utilization of several types of knowledge. The integration of frames, scripts, and rules within the system can provide the necessary flexibility to model the market and the policies which will influence the market.
3.4. Networked expert systems for economic policy analysis
In order to completely analyze an economic policy, one has to consider the influence of the government agencies, the producers, and the consumers. Before implementing a policy, a governmental agency would be interested in trying to determine how the consumer and the producer would react to the new policy. The producer on the other hand would be interested in determining how its competitors, the governmental agencies, and the consumers react to its policy. Clearly, the volume of knowledge which will have to be stored for a realistic examination of a policy would be extremely large and cannot be practically stored in a single expert system. Even if storage were practical, the time taken to search such a large knowledge set would be unrealistic. Therefore, one needs a networked expert system, with each governmental agency, producer, and consumer modelled within a specific system, all of which either interact through the processor, which is designated as the market, or directly interact between each other, depending on the situation. Fig. 1 illustrates the network. Ideally one would expect the interactions between the various entities to take place through the market. These links are specified through the solid lines in fig. 1. However, if it is mutually advantageous, there could be interactions between the various entities which bypass the market. These interaction links are indicated by the broken lines in fig. 1.
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expert sysiems
I-GO-------
\\------Fig. 1. Possible interactions
between
the entities in a market
In setting up such a framework one has to keep in mind that it would not be practical to include all the producers and all the possible consumers. From the perspective of a given producer for implementation purposes, one should therefore include all the producers who are currently viewed as competitors, the producers who are suppliers, and a prototype of a producer who could possibly enter the market and compete effectively. It would be impossible to effectively generate all possible categories of consumers. However, one could create profiles of consumer behavior, in certain income ranges along with certain attributes, such as consumers who tend to save or are risk-averse. Given the above characteristics of the network, one can effectively simulate a market and the implications of implementing a specific policy within it, the results of which would form the basis for actual implementation of the policy in the field. 4. Implementation
issues
In this section we will consider some of the implementation issues pertaining to knowledge possessed by each node and the interaction between the nodes. 4.1. Knowledge In setting up a framework such as that proposed in the previous section, there are two possibilities: 1) the interaction between Ci, Pi, and G, takes place only through the expert system marked E, which is the market, and
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2)there is direct interaction between C,, i = 1,. . . , n, or Pi, j = 1,. . . , m, or between Ci and Pi,leading to issues such as collusion. For the purposes of this paper we focus only on the first issue. Let us first consider the knowledge which has to be incorporated in each of the nodes. The knowledge pertaining to the consumers can be broken down into five classes: 1) income and investments and perceived changes in income flow or investments (e.g., salary increases), 2) consumers’ utility functions and knowledge of its utilization, 3) consumers’ heuristics about the behavior of the economy and how to take advantage of it, 4) consumers’ heuristics about the behavior of the producers and other consumers, and 5) the knowledge about the laws goveming consumer behavior. Knowledge relating to each of the classes can be encoded in frames. For example, the income class would have a collection of frames possibly linked together. As shown in fig. 2, the topmost frame in the slot could contain slots specifying the various sources of income. These slots could be pointers to other frames in which information about this particular income could be specified. For example, the salary information which is specified in the salary frame indicates two types of income, one from a regular job and the other from consulting. Subslots (or facets) associated with each slot give more information about the slot. Here, income from the jobs ($30,000) is specified in the subslot called ‘amount’. Similarly, the income from consulting is stated in its associated subslot. As shown in fig. 2, one could organize the frames in a hierarchy, with more detailed information being obtained as one goes down the hierarchy. For example, the stock frame specifies the companies in which the consumer holds stock, while information on each such company is stored in the next level frame set. An additional feature to note is that procedures can be attached to subslots. Such procedures can be utilized to compute or change values associated with a slot. In fig. 2, the subslots ‘if needed’ specify procedures which are executed when the slot values need to be determined. Though absent for simplicity, appropriate information would normally be present in the frames labeled ‘bond’, ‘interest’, and ‘company Y’. The knowledge the producers possess can also be categorized into five classes: 1) product production knowledge, 2) product cost and marketing knowledge (including knowledge relating to issues such as profit maximization), 3) heuristics about consumer and competitor behavior, 4) governmental rules and regulations and how they affect the production and selling of the products, and 5) cash flow and investment knowledge. Governmental agencies perform two functions. They set up the rules and regulations or specify policies which determine the interaction in the market place. Secondly, they monitor the behavior of the consumers and the producers in the marketplace to ensure that they follow the rules and regulations which have been specified by the agencies. The knowledge that each govemmental agency has is the current policy information within its own jurisdiction
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Fig. 2. An example of a hierarchy of frames
and knowledge allowing it to recognize that a particular consumer or producer is in violation of the policy. Additional features of the governmental agency expert systems are the abilities to modify the policies and to communicate them to the market. Thus, one can perceive the users of this network-based approach as essentially being associated with the governmental agency expert system. The user would therefore specify a new policy and communicate it to the market and then observe the behavior of the consumers and producers after the policy went into effect. This behavior can then be compared with prior behavior or other behavior as a result of changing the policy.
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The expert system which is modelled as the market does the broadcasting of information to the consumers and producers. Further it communicates the behavior information to the appropriate governmental agency. The knowledge possessed by this system includes 1) identification of consumer and producers and 2) the ability to respond to their messages (i.e., it possesses scripts which specify conditions under which the interaction between consumer and producer can take place and the results of the interaction are then broadcast to the appropriate consumers, producers, and governmental agencies). Scripts are illustrated in the next section when we consider an example of a market. 4.2. A networked expert systems example As described above, our network consists of three types of expert systems representing Ci, Pi, and G,. For illustrative purposes, suppose that two government agencies (one federal and one state) are potential regulators of the market for an agricultural product, corn. The federal agency, G,, has been charged with allocating up to $lO,OOO,OOO in price guarantees (potentially in the form of subsidies) to eligible growers of corn in a specified state. The individual state agency, G,, actually makes the allocation decisions but must conform to a set of specific rules set by the federal agency. Only farmers, P 1,. . . , P,, that meet specified acreage, crop history, and farm-derived income requirements are eligible for the program. Consumers, C,, . . . , C,,,, can, under a 3/4 majority, vote the program out. More importantly, they can alter their demand decisions concerning corn or related products (substitutes or complements). In order for a producer to participate in the subsidized market he/she has to be determined to be eligible. Thus, one script (fig. 3) would involve the qualification of a farmer as ‘eligible’ to participate in the program and thus determine a part of the communication that can take place across the network. This might entail the checking of the slots in the individual’s ‘land frame’ to insure, for example, that he/she owns at least 100 acres and has planted and harvested at least 50 acres of corn the last five years. Additionally, the ‘income frame’ will be checked to make sure that the individual obtains at least 75% of gross income from specified farm-related activities. Once it is determined that a farmer is eligible, the relevant communication links are made open but are not necessarily utilized. Farmers who do not qualify (or choose not to apply) for crop price guarantees may still produce and sell the crop. Their market differs from the price guarantee or subsidized market in that the price they receive is subject to the choice made by the consumers. The farmers participating in the subsidized markets are guaranteed a price which does not vary. Fig. 4 illustrates a script for the subsidized market. If the actual market equilibrium price falls short of the guaranteed price, the net subsidy is positive. If the
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: Eligibility : Corn Farmer : Land, Corn, Farm, income : Farmer, Government Agency
Entry Conditions : Fanner has more than 100 acres of land Farmer has harvested at least 50 acres in 5 years Farm related gross income is greater than 75% Result Conditions : Farmer is eligible for subsidy Farmer informed about subsidy restrictions if he/she XCePtS 1 Scene 1 :Application Farmer requests participation Farmer makes available relevant information Farmer waits for information to be processed Scene 2 :Evaluation Government agency evaluates provided iniormation Query farmer for further inlormation and resolve discrepancies in provided information Scene 3 :Eligibility Farmer is deemed eligible Information on restrictions on behavior is provided if substdy is accepted Ineligible Path : Farmer is deemed ineligible Fig. 3. Eligibility
script for corn farmers.
market equilibrium price exceeds the subsidized price, then the net subsidy is negative, with the government agency capturing the monetary gains. Consumers form the final group of players in our network of expert systems. With a knowledge base incorporating data such as the prices of other commodities, weather and harvest forecasts, individual income or budget constraints, and preference orderings, the consumers put demand information into the network. For example, consumers may react to producer-issued offers to sell or consumers may directly issue offers to buy. Allocation may occur through bidding mechanisms or through repeated posted offers. Individual consumers control their demand for end products, including corn, subject to their individual budget constraints. All participants are potential consumers with the government agencies possibly acting as buyers for later resale in order to minimize the cost of their programs. As the number of market activity possibilities (e.g., allocation mechanisms, buyer and seller activities such as futures contracts, option contracts, etc.) expands, this increases the number of scripts required to fully detail the expert system network. Within the networked system, we have the following steps taking place (with the understanding that steps marked by an asterisk can occur at any point
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:comMsrket :Suba&adcommatkat :CWmonaY : Farmers, Consumars,GovernmentAgancy
Entry Conditions : Producer has corn to sell Producer has been cla.ssiiiadas eligiila subsidiiad prkx is known Result condltlon8 : Producer has lass corn to sell Consumer has corn Producer has more money Consumer has lass money Scene 1 :Availablllty Producer spa&s corn availability Subsiiad price is broadcast Consumer spadfias corn amount naadad Consumer spacifk p&a willing to pay Scene2 :Tranaactlon Ifmumar price is equal to or greater than subsidy prim than transaction mada No tnnsactlon Path : Consumer informed prim too tow - no transaubn Scene3 :NoBuyem Noconsumer willing to pay subsidii prica Governmarl agancy buys at spscibd price Fig. 4. Subsidized corn market script
subsequent period): (1)
to their initial occurrence but prior to the end of the market
government agencies initialize (set) conditions for market period - these include conditions for farmer certification and subsidy price to be offered (possibly user input); (2) farmers meeting conditions choose to opt for program price or participate in free market (farmers use knowledge about their land, income, etc. as well as heuristics about the economy, weather, etc. to make this decision); (3) farmer yields determined, i.e., corn crop realized for participating and nonparticipating producers (this would be based on acreage and randomly assigned disasters); (4)* consumer and/or producer offers entered into market (offers would again be based on knowledge possessed by the participants); (5)* consumer and/or producer responses determining sales quantities and safes prices (responses based on knowledge about the domain); (6)* transference of funds as offers accepted - credit and debit update occurring each time offer accepted (script possessed by market specifies the procedure for transference of funds);
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(7)* revised or updated offers entered into the network followed by responses (scripts possessed by participants specify the sequence of offers to be made); (8) final market structure determination - either ‘clock’ on offers expires or no offers occur for specified amount of time. Thus, by specifying the appropriate knowledge within the expert systems one can observe the interactions which occur when a policy is implemented within the market.
5. Conclusions In this paper we have shown the possible use of expert systems as a tool for economic policy analysis. In order to model the interactions, we have proposed a networked expert system framework. The knowledge required to operationalize the framework would be stored at the various nodes using three knowledge representation schemes: rules, frames, and scripts. Although the current paper focuses on illustrating the interactions which occur within the market, the framework can easily be modified to take into account interactions between the various entities that could occur outside of the market. This would imply that, in addition to domain knowledge, one would have to incorporate knowledge about whom to communicate with for a specific purpose. In a networked-knowledge-based system, this knowledge has been characterized as ‘peripheral knowledge’ by Jacob and Pirkul (1988). Enhancing the knowledge at each node with peripheral knowledge will allow one to study the market impact of interactions taking place both inside and outside the market, thus expanding the potential usefulness of expert system modelling in economic analysis.
References Barr, A. and E.A. Feigenbaum, 1982, The handbook of artificial intelligence. Vol. II (William Kauffman, Los Altos, CA). Fikes, R. and T. Kehler, 1985, The role of frame-based representation in reasoning, Communications of the ACM 28, 906-920. Harmon, H. and D. King. 1985, Expert systems: Artificial intelligence in business (Wiley, New York, NY). Hoffman, E.. V. Jacob, J. Marsden, and A. Whinston, 1988, Expert systems and economic policy analysis: An application to OCS auction leasing, Computer Science in Economics and Management 1, 113-135. Hoffman, E., V. Jacob, J. Marsden, and A. Whinston, 1986, Artificial intelligence in economics - Expert systems modelling of microeconomic systems, in: L.F. Pau, ed., Artificial intelligence in economics and management (North-Holland, Amsterdam). Hudson, D.L. and T. Estrin, 1984, EMERGE, a data-driven medical decision making aid, IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 87-89.
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Jacob, VS. and H. Pirkul, 1988, A framework for networked knowledge based systems, IEEE Transactions on Systems, Man and Cybernetics, forthcoming. Patil, R.S. et al., 1981, Casual understanding of patient-illness in medical diagnosis, Proceedings of the seventh international joint conference on AI, 893-899. Rauch-Hindin, N.B., 1986, Artificial intelligence in business, science and industry, Vol. I (Prentice-Hall, Englewood Cliffs, NJ). Rich, E., 1983, Artificial intelligence (McGraw I-U, New York, NY). Shortliffe, E.H., 1976, Computer-based medical consultations: MYCIN (Elsevier, New York, NY). Sxolovits. P. and S.G. Pauker, 1978, Categorical and probabilistic reasoning in medical diagnosis, Artificial Intelligence 11,115-166. Waterman, D.A., 1986, A guide to expert systems (Addison-Wesley, Waltham, MA).