Artificial Intelligence and International Relations: An Overview Philip A. Schrodt
Artificial intelligence techniques have been used to model international behavior for close to twenty years. Alker and his students at MIT were generating papers throughout the 1970s (Alker and Christensen 1972; Alker and Greenberg 1976; Alker, Bennett, and Mefford 1980), and by the the early 1980s work at Ohio State was responsible for the first article to apply AI in a mainstream IR journal (Thorson and Sylvan 1982) and the first edited book containing a number of AI articles (Sylvan and Chan 1984). This volume provides the first collection of essays focusing on AI as a technique for modeling international behavior,1 an approach commonly, if controversially, labeled "AI/IR." The essays come from about twentyfive contributors at fifteen institutions across the United States and Canada; the substantial foci range from Japanese energy security policy to Vietnam policy in the Eisenhower administration. The purpose of this overview is twofold. First, it will provide a brief background of relevant developments in AI in order to provide some perspective on the concepts used in AI/IR. Second, it will identify some common themes in the AI/IR literature that use artificial languages for modeling. I will not deal with any of the issues in depth, nor provide extensive bibliographical guidance, as these are ably presented in the chapters themselves (e.g., those by Mefford and Purkitt in this volume; Mallery [1988] also provides an excellent introduction). I will not discuss the natural language processing (NLP) literature—which is covered in the chapters by Mallery, Duffy, and Alker et al.—though I will discuss projects that use text as a 1
Cimbala (1987) and Andriole and Hopple (1988) provide introductions oriented toward U.S. defense concerns, but these are only a narrow part of the field of IR generally.
source of information for development of models rendered in artificial language (e.g., Bennett and Thorson; Boynton; Sylvan, Milliken, and Majeski). Th chapter does not purport to provide a definitive description of the AI/I field; it is simply one person's view of the organization of the field at th moment. In contrast to many other modeling approaches, the AI/IR con munity is characterized by a healthy level of internal debate. This chapti is the overture, not the symphony. I intend only to draw your attention to themes; the details, in both melody and counterpoint, are found in the chapters that follow.
Artificial Intelligence The label "artificial intelligence" is, ironically, rejected by a majority of the authors in this volume as a description of their shared endeavor. Th preferred label is "computational modeling," which acknowledges the field intellectual roots in the formal modeling and computer simulation literatur within political science, rather than in the AI literature of computer science As will be noted below, the AI/IR efforts utilize only a tiny subset of AI methods, and in many respects AI/IR overlaps at least as much with cognitive psychology as with computer science. The AI label poses two additional problems. The most severe is guilt by association with "the AI hype": the inflated claims made for Al by th popular media, science fiction, and consulting firms. The AI hype has bee followed by the backlash of the "AI winter," and so AI/IR risks being caugh in a counterrevolution just as it is beginning to produce results. The second problem is the controversial word "intelligence." In the AI hype, "intelligence" has usually been associated with superior intelligent such as that exhibited by Star Wars robots (either the George Lucas or Ronald Reagan variety). The most common retort I encounter when presentin AI/IR overviews to unsympathetic audiences is: “You can't model politic using artificial intelligence;
you'd have to use artificial stupidity.”2 As the chapters that follow indicate, that is precisely our shared agenda! “Artificial stupidity” involves limited information processing, heuristics, bounded rationality, group decision processes, the naive use of precedent and memory over logical reasoning, and so forth. These features of human reasoning amply documented in the historical and psychological literature, are key to AI/IR but largely absent from optimizing models of the dominant forme paradigm in political science, rational choice (RC). Ironically, the true "artificial" intelligence is utility maximization, not the processes invoked ii computational models. All this being said, one must confront two social facts. First, the term "computational modeling" has not caught on because it is not reinforce:, by the popular media. Second, AI/IR has borrowed considerably from this part of computer science and the cognitive sciences which calls itself "artificial intelligence," including the widespread use of LISP and Prolog as formal languages, the formalization of rules, cases and learning, and a great deal of vocabulary. In the spirit of mathematician David Hubert's definition of geometry as "that which is done by geometers," the AI label will probably stick.
AI in the Early 1980s The term "artificial intelligence" refers to a very large set of problems and techniques ranging from formal linguistic analysis to robots. Researchers in "AI" may be mathematicians or mechanics, linguists or librarians, psychologists or programmers. Schank (1987:60) notes:
Most practitioners would agree on two main goals in AI. The primary goal is to build an intelligent machine. The second goal is to find out about the nature of intelligence. . . . [However,] when it comes down to it, there is very little agreement about what exactly constitutes intelligence. It follows that little agreement exists in the AI community about exactly what AI is and what it should be.
2
This seemingly original joke seems to occur to almost everyone. . . .
Research in AI has always proceeded in parallel, rather than serially, with dozens of different approaches being tried on any given problem. As such, AI tends to progress through the incremental accumulation of partial solutions to existing problems, rather than through dramatic breakthroughs. Nonetheless, from the standpoint of AI/IR, there were two important changes in Al research in the late 1970s. First, rule-based "expert systems" were shown to be able to solve messy and nontrivial real-world problems such as medical diagnosis, credit approval, and mechanical repair at the same level of competence as human experts (see, for example, Klahr and Waterman 1986). Expert systems research broke away from the classical emphasis in AI on generic problem solving (e.g., as embodied in chessplaying and theorem-solving programs) toward an emphasis on knowledge representation. Expert systems use simple logical inference on complex sets of knowledge, rather than complex inference on simple sets of knowledge. The commercial success of expert systems led to an increase in new research in AI generally —the influx of funding helped— and spun off a series of additional developments such as memory-based reasoning, scripts, schemas, and other complex knowledge representation structures. Second, the personal computer, and exponential increase in the capabilities of computers more generally, brought the capabilities of a 1960s mainframe onto the researcher's desk. The small computers also freed AI researchers from dependence on the slow and idiosyncratic software development designed for centralized mainframes. The "AI style" of programming led to a generation of programmers and programming environments able to construct compl cated programs that would have been virtually impossible using olde languages and techniques. All of this activity lead to a substantial increase in the number of peopl doing Al. The American Association for Artificial Intelligence (AAAI) wa founded in 1979, had 9,935 members by 1985 and 14,269 by 1986 — growth of 43 percent in a single year. In short, AI in the 1980s wa accompanied by a great deal of concrete research activity in contrast to faddish techniques such as catastrophe theory.
The AI Hype Perhaps predictably, the increase in AI research was accompanied (and partially fueled) by a great deal of hype in the popular and semiprofessiona media. An assortment of popular books on AI were produced by researchers such as Feigenbaum (Feigenbaum and McCorduck, 1983; Feigenbaum and McCorduck and Nil, 1988), Minsky (1986), and Schank (Schank and Riesback, 1981); at times these reached sufficient popularity to be featured by paperback book clubs. Journalists such as McCorduck (1979), Sanger (1985), and Leithauser (1987) provided glowing appraisals of Al; these are only three of the hundreds of books and popular articles appearing in the early to mid-1980s. Concern over the Japanese "Fifth Generation Project" (Feigenbaum and McCorduck, 1983) provided impetus for the wildly unrealistic3 "Strategic Computing Initiative" of the Defense Advanced Research Projects Agency (DARPA, 1983). These popular works provided a useful corrective to the outdated and largely philosophical criticisms of Dreyfus (1979) and Weizenbaum (1976) about the supposed limits of AI. By the early 1980s researchers had made substantial progress on problems that by any reasonable definition required "intelligence" and were exhibiting performance comparable to or exceeding that of humans. However, the popularizations were understandably long on concepts and short on code, and their explicit or implicit promises for continued exponential expansion of the capabilities of various systems did not take into account the tendency of technological innovation to follow a logistic curve.4 Because the promises made in these popularizations were
3
For example, DARPA's 1983 timetable calls for the following developments by 1990: vision subsystems with "1 trillion Von-Neumann equivalent instructions per second"; speech subsystems operating at a speed of 500 MIPS that "can carry on conversation and actively help user form a plan [sic]," and "1,000 word continuous speech recognition." Each of these projected capabilities is 10 to 100 times greater than the actual capabilities available in 1990, despite massive investments by DARPA. For additional discussion, see Waldrop (1984); Bonasso (1988) provides a more sympathetic assessment. 4 A 10,000-rule expert system is unlikely to achieve ten times the performance of a 1,000-rule system. To the contrary the 1,000-rule system will probably have 80-90 percent of the functionality of the large
based largely on laboratory results that had not been scaled up nor widely applied in real-world settings, such promises set up AI for a fall.
The AI Winter The hype of the mid-1980s leveled off by the latter part of that decade and some segments of the AI community —particularly companies producing specialized hardware— experienced the "AI Winter." However, the decline of AI was more apparent than real and reflected the short attention span of the popular press as attention turned away from AI to global warming, superconducting supercolliders, parallel processing, and cold fusion. Experimental developments, most notably neural networks, continued to attract periodic media attention, but the mainstream of AI assumed a level of glamor somewhere between that of biotechnology and X-ray lasers: yesterday's news, and somewhat suspect at that. Yet ironically, the well-publicized bankruptcies of "AI firms" (see, for example, Pollack 1988) were due to the success rather than the failure of AI. As AI techniques moved out of the laboratories and into offices and factories, commercial demand shifted from specialized "AI workstations" and languages such as LISP to systems implemented on powerful general-purpose microcomputers using standard procedural programming languages such as C or off-the-shelf expert systems shells. AI research moved in-house and was diffused into thousands of small applications rather than a few large ones. Overall, the AI field remained very healthy. Although membership in the AAAI declined in 1988 and 1989, dropping to 12,500 members, the 1989 International Joint Conference on Artificial Intelligence, the AI equivalent of the International Political Science Association, was large enough to require the Detroit Convention Center, fill every convention hotel in downtown Detroit and nearby Windsor, Ontario, and all this despite a $200 conference registration fee. system; the additional 9,000 rules are devoted almost exclusively to the residual 10-20 percent of the cases.
Beyond the issues of popular perception, it is important to note that the future of AI/IR is largely independent of the successes or failures of AI generally. Whether a chess-playing program will be able to defeat the reigning human grand master or whether simultaneous translation of spoken language is possible will have no effect on most AI/IR research. Even if mainstream AI has some implications for the development of computational models of international behavior, the AI/IR literature is primarily shaped by literatures in psychology, political science, and history rather than computer science. The techniques borrowed from computer science are only tools for implementing those theories.
AI/IR Research: A Framework This section will attempt to structure the various sets of problems studied in AI/IR. For example, there has been frequent confusion outside the field as to why discourse analysis (represented in this volume by Boynton; Thorson and Bennett; and Sylvan, Milliken, and Majeski) should have anything to do with rule-based models (e.g., Job and Johnson) because the techniques are entirely different. The simple answer is that the AI/IR literature is primarily linked by underlying theories and questions rather than by methodology. Although this is consistent with classical Kuhnian notions of science it is decidedly uncharacteristic of formal approaches to the study of international behavior such as correlational analysis, arms-races models, an game theoretic models of war initiation, which are largely linked by technique. As noted earlier, any effort to find common themes in a field as conceptually rich and disputatious as AI/IR is fraught with the risk of oversimplification This chapter is simply a survey of the high points. AI/IR developed in a evolutionary fashion; the organization I have presented below is a typolog imposed, ex post facto, on an existing literature, rather than an attempt to present a consensus view of where the field is going. The typology consists of three parts. The first category is the research on patterns of political reasoning, which provides the empirical groundini for models of
organizational decision making. The second category involve the development of static models of organizational decision making, which aim to duplicate the behavior of an organization or system at a specific point in time. This is the largest part of the literature in terms of model: that have actually been implemented, and it relies on the expert system: literature in the AI mainstream. The final category contains dynamic model: that incorporate precedent, learning, and adaptation, which can show how an organization acquired its behavior as well as what that behavior is. These models are necessarily
more
elaborate
and
experimental,
though
some
large
scale
implementations exist, notably the JESSE model of Sylvan, Goel, and Chandrasekaran.
Patterns of Political Reasoning The Psychological Basis. Virtually all work in AI/IR acknowledges ar extensive debt to experimental work in cognitive psychology. Although a wide variety of approaches are cited, two literatures stand out. The first influence is the work of Allen Newell and Herbert Simon or human problem solving (Newell and Simon 1972; Simon 1979, 1982). Simon's early work pointing to the preeminence of satisficing over maximizing behavior is almost universally accepted in AI/IR, as is the Newell-Simon observation that human cognition involves an effectively unlimited (albeit highly fallible) memory but a fairly limited capacity for logical reasoning These assumptions about human problem solving are exactly opposite those of the rational choice approach, where cognition involves very little memory but optimization is possible. In addition to these general principles, othei work by Newell and Simon on specific characteristics of human problem solving is frequently invoked, for example the re-use of partial solutions and the distinction between expert and novice decision making. The second very large experimental literature is the work of Danie Kahneman, Paul Slovic, Amos Tversky (KST), and their associates in exploring the numerous departures of actual human decision making from the characteristics predicted by
utility maximization and statistical decision theories (Kahneman, Slovic, and Tversky 1982). This work has emphasized, for example, the importance of problem framing, the use of heuristics, the effects of familiarity and representativeness, and so forth. Even though the experimental results, general principles, and concepts of these two literatures are used extensively in AI/IR, their theoretical frameworks are not. Newell and his students have developed a general computational paradigm for AI, SOAR (Laird, Rosenbloom, and Newell 1986; also see Waldrop 1988), but to my knowledge it has not been applied in the IR context. "Prospect theory," the term usually applied to the KST work, is also rarely used. These research results are used instead to explicate some of the characteristics of IR decision making, which, because it is organizational and frequently involves unusual circumstances such as the decision to engage in lethal violence, is far removed from the individualistic studies of much of the psychological literature. Some work on group decisionmaking dynamics has also been used—for example, Pennington and Hastie on decisions by juries (cited in Boynton)—but in general this literature is smaller and less well known in cognitive psychology than the theories and experiments on individuals. Knowledge Representation. Consistent with the Newell-Simon approach, AI/IR models are heavily information-intensive. However, the theory of data employed in AI/IR is generally closer to that of history or traditional political science than it is to statistical political science. Most of the chapters in this volume use archival text as a point of departure; the remainder use secondary data such as events that were originally derived from text using procedures similar to content analysis. The ordinal and interval-level measures common to correlational studies, numerical simulations, and expected utility models are almost entirely absent. This, in turn, leads to the issue of knowledge representation, which is a theme permeating almost all of the papers and which accounts for much of their arcane vocabulary and seeming inconsistency. Whereas behavioral political analysis essentially has only three forms of knowledge representation —nominal, ordinal, and interval variables— AI presents a huge variety, ranging from simple if . . . then
statements and decision trees to scripts and frames to self-modifying programs and neural networks. This surfeit of data structures is both a blessing and a curse. It provides a much broader range of alternatives than are available in classical statistical or mathematical modeling, and certainly provides a number of formal structures for representing the large amounts of information involved in political decision making; this comes at the expense of a lack of closure and an unfamiliar vocabulary. Part of this problem stems from the fact that knowledge representation concepts have yet to totally jell within their parent discipline of computer science. In this regard AI/IR is quite different than the behaviorialist adoption of statistical techniques and the RC adoption of economic techniques: In both cases stable concepts and vocabulary were borrowed from more matur fields. Structure of Discourse and Argument. For the outsider, perhaps the most confusing aspect of the AI/IR literature is the emphasis on the analysis o political argument and discourse. This type of analysis is found in the article: by Boynton; Sylvan, Milliken, and Majeski; and Bennett and Thorson; more sophisticated tools for dealing with discourse are found in the natural language processing (NLP) articles. At first glance, rummaging through the Congressional Record or using Freedom of Information Act requests to uncovei Vietnam-era documents is the antithesis of the formal modeling approach Archival sources are the stuff of history, not models. In fact, these analyses are at the core of modeling organizational cognition. As such the archival work is simply specialized research along the lines of the general psychological studies. The political activities modeled in AI/IR are, without exception, the output of organizations. Because the AI/IR approach assumes that organizations are intentional and knowledge-seeking, it is important to know how they reason. Conveniently, organizations leave a very extensive paper trail of their deliberations. Although archival sources do not contain all of the relevant information required to reconstruct an organization's behavior —organizations engage in
deliberations that are not recorded and occasionally purposely conceal or distort the records of their deliberations— it is certainly worthy of serious consideration.5
Static Modeling: Rule-Based Systems Rule-based systems (RBS) are currently the most common form of AI/IR model, and even systems that go well beyond rules, such as the JESSE simulation, contain substantial amounts of information in the form of rules. Contemporary RBS are largely based on an expert systems framework, but the "production systems" that dominated much of AI modeling from the late 1950s to the early 1970s are also largely based on rules; early production system models of political behavior include Carbonell (1978) in computer science and Sylvan and Thorson (1982) in IR. In addition to chapters by the authors in this volume, other models of international behavior using the rule-based approach have included Soviet crisis response (Kaw 1989), Chinese foreign policy (Tanaka 1986), the political worldview of Jimmy Carter (Lane 1986) and Chinese policy toward Hong Kong (Katzenstein 1989). In its simplest form an RBS is just a large set of if . . . then statements. For example, a typical rule from Job and Johnson's UNCLESAM program —a simulation of U.S. policy toward the Dominican Republic— has the form
IF U.S. Posture to the Dominican Republic government > 4 and Stability Level >= 5 and Stability Level Change > 0 THEN increment CIS. Use of Force Level by 1 An RBS may have hundreds or thousands of such rules; they may exist independently, as in Job and Johnson or production system models, but more 5
Conceptually, this effort is similar to the "cognitive mapping" methodology pursued in Axelrod (1976); the "operational code" studies (e.g., George 1969; George and McKeown 1985) are other antecedents.
typically are organized into hierarchical trees (for example, Hudson in this volume or Kaw 1989). Typical commercial expert systems used for diagnosis or repair have about 5,000 rules; most AI/IR systems are far simpler. Mefford's chapter in this volume describes in considerable detail RBS developments beyond basic if . . . then formulations; one should also note that the boundaries between the more complicated RBS and other types of models (for example, case-based reasoning and machine learning systems) are extremely fuzzy. Nonetheless, virtually all Al models encode some of their knowledge in the form of rules.6 Despite the near ubiquity of rules in computational models, this approach stands in clear contrast to all existing formal modeling traditions in political science, which, without exception, use algebraic formulations to capture information. These methods encode knowledge by setting up a mathematical statement of a problem and then doing some operations on it (in RC models, optimization; in statistics, estimation; in dynamic models, algebraic solution or numerical simulation). The cascading branching of multiple rules found in RBS is seldom if ever invoked; when branches are present they usually only deal with boundary conditions or bifurcations 7 and are simple in structure. Although much of the impetus for the development of RBS in political science came from their success in the expert systems literature, rules are unusually well suited to the study of politics, as much of political behavior is explicitly rulebased through legal and bureaucratic constraints. Laws and regulations are nothing more than rules: These may be vague, and they certainly do not entirely determine behavior, but they constrain behavior considerably. Analyses of the Cuban Missile 6
Neural networks are the primary exception to this characteristic and are a current research focus precisely because they offer an alternative to rule-based formulations. 7
For example an action A would be taken in the expected utility formation E(A) = p(100) + (l-p) (-50) if and only if p > 1/3; the dynamic model xt+1 = axt + b is stable if and only if lal
Crisis, for example, repeatedly observe that the military options were constrained by the standard operating procedures of the forces involved. Informal rules —"regimes" or "operational codes" in the IR literature (e.g., Krasner 1983; George 1969)— impose additional constraints. For example, in the Cuban Missile Crisis, John F. Kennedy did not consider kidnapping the family of the Soviet ambassador and holding them hostage until the missiles were removed, though in some earlier periods of international history (e.g., relations between the Roman and Persian empires, circa 200 C.E.) this would have been considered acceptable behavior. In short, rule-based political behavior is not an "as if" proposition: I can be empirically confirmed. However, because bureaucracies do not solely, follow their rules— in fact most bureaucracies would be paralyzed if the, attempted to do so—the extent to which rules can capture actual behaviour and the complexity required to do so is an open question. As the chapter in this volume and other RBS research indicate, it is clearly possible I simulate political behavior using rules. The complexity of these system, though substantially greater than the complexity of most existing formal models (other than simulations) is also well within the limits of existing RBS developed in other fields. Despite the widespread use of rules in AI/IR models, many of the chapter in this volume argue against rule-based formulations, or at least indicate problems with the use of rules. This should not be interpreted as a rejection of any use of rules but only as a rejection of depending solely on rules. This, in turn, is a reaction to theoretical issues in AI rather than political science: A variety of approaches advocate rules in some form as a universal standard of knowledge representation and argue that all work in AI should use a single, unified concept of knowledge representation and manipulator. This comprehensive approach is rejected by almost all of AI/IR work as premature at best, given the evidence from the cognitive and organization, decisionmaking literature.
Dynamic Modeling: Adaptation and Learning Learning is one of the most basic characteristics of human cognitive behavior but has been largely absent from existing formal models in political science.8 One of the most distinctive—and potentially revolutionary—characteristics of the AI/IR models is the use of learning as a dynamic element. By attempting to model not only what organizations do but why they do it— in the sense of providing an explanation based on the prior experience of the organization—these models can potentially provide greater detail and process validity than those currently available. An assortment of learning schemes are currently under development, but most involve at least two elements. First, the basic rule of learning is "Bureaucracies do not make the same big mistake twice." Reactions to a situation are based in part on precedents, and the success or failure of a particular plan will affect whether it is used in the future. Knowledge is modified, not merely acquired. The second general characteristic is the reapplication of solutions that worked in the past to problems encountered in the present: In the JESSE simulation this is called "compiled reasoning." As Mefford notes, this same concept is central to Polya's scheme of human problem solving; more recently it has been the central focus of the Laird, Rosenbloom, and Newell SOAR project. The content of an organization's compiled reasoning in turn depends on the problems it has previously encountered, and so the history of the organization is important in determining what it will do. This emphasis on learning and adaptation provides the "path dependence" mentioned in a number of the chapters. International politics is not like a game of chess where future plays can be evaluated solely on the basis of the current board position. Instead, how a situation came into being may be as important as its static characteristics. Deductive reasoning from first principles alone is not sufficient. Learning also partially resolves the problem of underdetermination in satisficing —it 8
Formal models of simple learning are common in the psychological literature, but these are largely inappropriate to the organizational learning of complex, ill-defined tasks common to political settings.
indicates not just the suboptimal character of decisions but also substantially narrows which suboptimal decision will be made. Learning is such a basic human activity that it is taken for granted in the informal bureaucratic politics literature, though in recent years a number of studies reemphasizing the importance and problems of learning as a separate activity have emerged (see, for example, Neustadt and May 1986 on precedent and analogy; Etheredge 1985 on learning in foreign policy; Margolis 1987 on patterns). The difference is that the AI/IR models have succeeded in formally modeling this learning process in a complex environment, which no other models have.9 To the extent that learning and adaptation are key human behaviors, this is a major step forward. Perhaps because learning is so inherently human, the extent to which it can be simulated is frequently underestimated, if not rejected outright. One of the most popular—and most inaccurate—characterizations of computer models is "A computer can't do anything it wasn't programmed to do." Strictly speaking, this may be true: A computer can't do in five minutes anything a human with a pencil and paper couldn't do in eleven centuries of sustained effort,10 but for practical purposes there is a qualitative difference. As a complex system, a computer can very easily be programmed to perform in ways unanticipated by its programmers. By that same token one can reject another hoary characterization, "Computers can't be creative." In fact, there are a variety of rather straightforward techniques by which programs can "create" solutions to problems that were unanticipated by their programmers, and the creation of such programs has been a longstanding research tradition in mainstream AI. 9
Learning is another aspect acquired from the Al tradition. One of the key elements of LISP, the dominant Al programming language, is the absence of a distinction between program and data. As a consequence programs could be "selfmodifying," changing to adapt to a problem at hand. This is the essence of learning, and so models drawing on the Al tradition had a rich set of learning concepts at its core, whereas learning was difficult to add to RC or dynamic models. 10
Figuring eight-hour days and a sustained rate of 100 calculations per hour for the human; a very modest 1 MIPS for the computer.
Modeling organizational learning involves modeling at least two different types of learning. The first, and simpler, learning is knowledge acquisition: the process by which precedents, analogies, cases, plans, or whatever are acquired and retained. The second and more difficult phase is modeling the adaptation of the means by which those are invoked. In other words, the process of "reasoning by analogy" involves both the availability of analogous situations and also the means by which the analogy is made: Neither issue is self-evident. Consider, for example, the situation of a failure of reasoning by anal in the Bay of Pigs invasion. This exercise was modeled on the earlier successful CIA overthrows of Mossadegh in Iran and Arbenz in Guatemala. Had the Marines been involved in the planning, analogies might have bt made to the problems of invading islands encountered in World War leading, presumably, to greater skepticism or at least better preparation. Hence one could say that the Bay of Pigs failed because only “overthrow” precedents were used to the exclusion of "invasion" precedents. Alternatively, one could argue that the situation itself had been incorrectly understood in the sense that important variables were not considered: The theory that identified the precedents was in error. If the prevailing thee indicates that two things should match and they did not, the organization will recognize that something is wrong with the theory and change Arguably, this occurred historically: The failure at the Bay of Pigs was attributed to the lack of sufficient force and a clear U.S. commitment, and therefore the next two U.S. interventions in the Third World— in the Dominicc Republic and Vietnam—involved overt use of large numbers of troops. These two types of learning have very different implications for th structure of the model, and the question as to which dominates is a empirical one. Modeling the use of precedent is relatively straight forward but organizational modifications to the interpretation of precedent may be more important, particularly when dealing with unexpected behaviors. Precedent. The concept of precedent is probably second only to that of "rules" in the discussions in this volume, and precedent is closely linked to the issue of
memory. In the simplest form, actors will simply do wha they've done before, and the best predictor for behavior in a complex system is the patterns of history. Precedent, analogy, and case studies have strong antecedents in the traditional political literature (e.g., Neustadt and May 1986), as well as being formally invoked in Anglo-American legal reasoning. As with the use of rules, precedent does not require "as if" apologies: It is used openly and explicitly. The use of precedent is, however, somewhat ambiguous. In foreign policy discourse, precedent is most likely encountered as a justification—in othier words, it is invoked as an empirical regularity. In legal reasoning and in much of the casebased reasoning literature, in contrast, it is a plan a series of actions which should be taken. In the latter sense a precedent is merely a complex antecedent clause of an if . . . then clause with a complex consequent. These two uses of precedent are not mutually incompatible in a sufficiently regular and well-defined system, the use of precedents as plans would cause them to become empirical regularities—but they are different. As noted in several of the chapters, empirical studies of actual policydeliberations find little evidence of a dominant role for precedent. Several factors may account for this. First, in contrast to the legal arena, the international system is neither well defined nor particularly regular, and consequently the precedents are not necessarily clear. Furthermore, in IR discourse, a precedent such as "Munich," "Pearl Harbor," or "Vietnam" is most likely to be invoked as something to be avoided, not to be implemented.11 Precedents used repeatedly are incorporated into the standard operating procedures of the organization —they become rules. Consequently, precedent may be a powerful tool for decision making even if it isn't actively invoked 11
This can occur for normative as well as pragmatic reasons: For example, Allison (1971:197) emphasizes the strong negative impact the Pearl Harbor analogy had on Robert Kennedy's assessment of the option of bombing Cuba during the Cuban Missile Crisis. "I now know how Tojo felt when he was planning Pearl Harbor," Kennedy wrote during an ExCom meeting, and he would later write "America's traditions and history would not permit . . . advocating a surprise attack by a very large nation against a very small one."
in debate. The second possible problem is that precedents are probably generalized into "ideal cases." If a decision-maker refers to the danger of a coup in El Salvador, what is usually invoked is not a specific coup12 but rather coups in general. Compiled Reasoning and Structure. On the surface, compiled reasoning is only a modest enhancement of existing psychological models: The notion that individuals and organizations reuse prior successful behavior is not particularly controversial and is certainly strongly supported by experimental evidence with individuals going back several decades. However, the critical contribution of such models may be in the specification of "learning-driven models" (LDMs) —models whose key dynamics are determined by learning. In particular, this might begin to concretize the heretofore exceedingly mushy concept of "structure" in political behavior. For example, suppose that at any given time, the actors in a system can be viewed (or modeled) as simple stimulus-response actors—ceteris paribus, given an input, one can predict the resultant behavior. This, in turn, provides a set of mutual constraints on those activities, which we refer to as "structure." For example in the 1970s, any Eastern European state, on the basis of Soviet activities in Berlin, Hungary, and Czechoslovakia during the 1950s and 1960s could reasonably assume that excessive economic and political liberalization would lead to Soviet military intervention. From the standpoint of the actor, this is simply a rule of the system. However, in an LDM, every event has the potential of changing those reaction functions —in other words, the system is self-modifying (either through accumulation of cases or modification of rules). Although most events do not change the functions, when learning occurs, the reaction functions of the system may change dramatically. For example, by 1989, Eastern European states reacted as if economic and political liberalization would not cause Soviet intervention after some initial 12
Unless there is a clear and obvious precedent: The future of Ferdinand Marcos in the Philippines was discussed in terms of Marcos as "another Somoza," referring to the Nicaraguan dictator whose fall led to the regime. Usually, however, the search for precedent eStablishment of the Sandinista regime. Ussualy, however, the search for precedents does not go very deep.
experimentation by Poland along these lines was reinforced. Because some —if not most— of the knowledge of structure is embodied in complex qualitative structures, the change is not necessarily incremental, and it may be mutually reinforcing (as happened, for example, in Hungary, Poland, and the GDR in the autumn of 1989, followed later by Czechoslovakia, Bulgaria, and Romania) so as to cause a series of fairly dramatic changes. This does not, however, mean that the situation is chaotic, a key advant of LDMs over the other formulations. Most of the knowledge base and learning mechanism has not changed;13 only the output has changed. ' environment, the actors, and the parameters of their decision making almost unmodified; the change is embodied in relatively simple and predicte modifications of knowledge and rules for interpreting that knowledge, of empirically available in the archival record. Realistically modeling this type of behavior is a difficult task, and the AI/IR system that comes closest embodying it, unsurprisingly, is the complex JESSE simulations. But this objective underlies many of the chapters.
The Upshot: The Ideal Model To summarize these points, the following is a list of the characteristics I believe most researchers would include in an ideal AI/IR model.
The model would be consistent with the psychological literature on individual and group decision making, in particular it would involve suboptimal reasoning, heuristics, anchoring, and other aspects of "artificial stupidity."
13
In contrast consider the treatment of international crisis found in Snyder and Diesing (1977) which uses an RC approach. For a given configuration of payoffs at any stage in acrisis, Snyder and Diesing can analyze, using game theoretic concepts, the likely behavior of the actors in the crisis (or, more likely, induce the payoffs from the behavior). But this approach does not provide a means of moving from one game matrix to the next; It predict only a single decision, not a succession of decision. An LDM approach, in contrast, would seek to model the change in the payoffs as well as the decisions themselves. In a much simpler framework, the “sequential gaming” models of the RC tadition are also trying to attack this problem using algebraic methods.
Both the actions predicted by the model and the reasoning reflected in the determination of those actions would not be inconsistent with that found in actual political debate.
The model would store knowledge in complex, qualitative data structure such as rules, scripts, frames, schemas, and sequences. Among that knowledge, though not necessarily dominant, would be precedents an cases.
The model would learn from both successes and failures: Successful solutions would be likely to be reused; unsuccessful solutions or observations about changes in the environment would cause changes in the rules that are used to solve problems. This learning would be similar to that seen in actual political behavior.
The overall model would provide a general engine for the study of international politics —in other words, it would work on a variety of problems. The specific knowledge required to implement the model, of course, would differ significantly with the problem. None of the existing models incorporates all of these factors, and none is
considered even close to a final solution to them. However, almost all of the chapters in this volume contribute to this agenda, and quite a number of the components have already been demonstrated.
AI and Contemporary Political Science One of the most intriguing aspects of the AI/IR research is the breadth of the substantive foci of these studies. The topics dealt with in this volume include the Johnson administration's Vietnam policy, Dwight Eisenhower's Vietnam policy, Japanese energy security policy, development, (J.S. policy in the Caribbean, international relations during the early Cold War period, the international behavior recorded in the BCOW and CREON data sets, the Senate Foreign Relations
Committee on the Persian Gulf in the 1980s, the Soviet intervention in Hungary, and Luttwak's theory of coups d'etat. This list reads like the topics of any randomly chosen list of articles on international relations, in distinct contrast to the arms race and world modeling literatures, which have focused on fairly narrow types of behavior. By that same token, there is a strong empirical focus in virtually all of the studies. The research is grounded in the detailed study of actual political behavior, not in techniques borrowed from economics, computer science, or mathematics.
AI and Rational Choice The most important alternative model to that proposed in the AI/IR approach is the rational choice approach, which presumes that political behavior, like economic behavior, can best be modeled by assuming individuals optimize their choices within a system of preferences and constraints without any higher cognitive processes. RC uses, for the most part, expected utility decision making and game theory, and is important both because of its dominant role in explaining domestic behavior and increasing application as a theory of international behavior.14 In addition to its role as "straw man of choice" in contemporary discourse in political science, RC provides an alternative to AI/IR in positing an explicit cognitive mechanism, in contrast to statistical studies and most dynamic models, which merely posit regularities. This debate between RC and cognitive psychology is not confined to IR but is found generally in the social sciences: Hogarth and Reder (1987) provide an excellent survey of the arguments; much of the KST agenda is directed toward falsifying RC
14
Supporters of the rational choice approach frequently consider it to be as pervaise in IR as in domestic politics. Although it has dominated one problem --the counterfactual analysis of nuclear war and nuclear detterence (see Brams 1985)—it is a relatively recent newcomer in the remainder of IR, dating mostly from the work of Bueno de Mesquita and his students, and more recently “crossover” such as Ordeshook and Niou (see Ordeshook 1989). Even this work deals primarily with a single issue, war innitiation. One need only compare the scope of the arms races bibliography of Anderton (1985), with 200+ entries, or the dynamic simulation literature (e.g., Guetzkow and Valdez, 1981) to see how relatively small the RC literature is in IR. Outside of IR, of course, RC is clearly the dominant formal paradigm in political science.
assumptions; Simon (1985) deals explicitly with these issues in terms of political science. Although in general AI/IR is viewed as competition to RC —and this is the position taken by most authors who address themselves to the issue— the possibility remains that the two approaches are complementary: Problems appropriate for an RC framework might be inappropriate for Al and vice versa. Most of the discourse analysis and NLP articles would probably concur with this characterization, and NLP tends not to address the RC issue at all. Using AI in a situation that can be accurately described by optimization using preferences and constraints would be using a sledgehammer to kill a fly: The problem of nuclear deterrence comes to mind. In relatively static situations of reasonably complete information, limited bureaucratic infighting quantitative or strictly ordered payoffs, and repeated plays, the expects value framework may be completely appropriate. This assumption of complementarity is the position of many economisl who are now using computational modeling: They maintain the basic RC concepts and vocabulary but use computational models to deal with phenomena such as memory and learning that confound existing mathematics techniques. If one can take the structuring of a situation as known and static —for example, the voting decision in a stable liberal democracy doesn’t include the option of changing the rules of voting — then RC will predict behavior quite efficiently. However, when self-modification is an option, simple RC models aren't very useful. Alternatively, the two approaches may be simply competitive; this view is implicit in most of the chapters in this volume that discuss RC. Fundaments to virtually all critiques of RC is criticism on empirical grounds: The "as if" approach, though possibly acceptable when formulated by Milton Friedman in 1953, is unacceptable when viewed in the light of thirty years of experimentation that has failed to validate those assumptions except unds highly artificial settings. The AI/IR literature, with its base in experiment, psychology and its heavy use of primary data,
provides an alternate formulation of political rationality with a superior empirical grounding. In evaluating this debate, note that the difference between AI/IR and RC does not lie in the assumption of preferences and goal-seeking behavior. The crucial difference is in the issue of optimization: RC assumes utilit maximizers; AI/IR assumes individuals and organization have inadequate information-processing abilities to optimize. In addition, AI/IR generall assumes a much more complicated and dynamic world than RC theories, which it can do because the models are computational rather than algebraic. For example, RC models usually assume that the consequences of actlor are known (with a known degree of uncertainty), whereas AI/IR approaches may try to model the acquisition of that knowledge. AI/IR, following KST postulates that the framing of a problem is a very important phase of finding the answer and, in fact, may well determine the answer. Following the SimonNewell tradition, memory is not considered a constraint either in the cognitive theory or the computational model, so huge amounts of information in complex structures can be used. In this sense, AI/IR and RC are mirror images: AI assumes sophisticated memory but limited processing; RC assumes sophisticated processing (optimization) but limited memory. On the down side, AI has far less mathematical closure than RC. RC’s intellectual coherence stems in no small part from a series of mathematical bottlenecks imposed by the available techniques. Common mathematical tools such as fixed-point theorems and results from game theory severely restrict the assumptions that can be made if a model is to be mathematically tractable. On the one hand, this gives RC an "assume the can opener" air;15 on the other hand, it provides a 15
For the benefit of those few who do not understand this allusion, the underlying joke goes as follows: A Physicist, a Chemist, and an Economist were stranded on a desert island. Exploring the island, they found a case of canned food but had nothing to open it with. They decide to share their expertise and, being academics, each gave ashort lecture on how their discipline would approach the problem. The Physicist began, “We should concentrate sufficient mechanical force to separate the lid from the can … “ The Chemist began, “We should create a corrosive agent to dissolve the lid from the can. …” The Economixt began, “First assume, we possess a can opener …”
large body of shared assumptions so that many new results are widely applicable. RC has a cumulativeness that is less evident in AI/IR. Because AI is algorithmic rather than algebraic, there are virtually no constraints on the complexity of the underlying argument; the techniques, in fact, tend to be data-limited rather than techniquelimited, a problem shared with numerical simulations, as Mefford notes.
AI/IR and Data The AI/IR approach is strongly empirical, another aspect that differentiates it from the RC tendency to refine theoretical concepts with little concern for empirical testing. From a sociology of science standpoint, the empirical focus of AI/IR is unsurprising given its dual roots in experimental psychology and the fact that most of the early researchers, from Alker onward, initially studied politics using behavioralist statistical methodologies. The AI/IR approach is developing, from the beginning, testable theories with the corrective feedback empirical studies provide. The data used in AI/IR resembles that of historical/archival research more than that of the quantitative statistical traditions of the 1960s and 1970s. The majority of the chapters in this volume use primary text or interviews as their data source, and they also emphasize issues such as context, structure, and discourse. Even those studies not using text (for example, Hudson or Schrodt) utilize very detailed descriptive sequences with thousands of points, a distinct contrast to the annualized forty-year time series typical of contemporary statistical studies. Almost all existing political research has concentrated on simple data structures, usually the rectangular case/variable data array. Even when more complicated structures were built —as, for example, in factor analysis or Guttman scaling— these were done within the constraints of linear models. However, it seems obvious that political behavior involves complex data structures such as rules and hierarchies, scripts and sequences, plans and agendas. The rational choice tradition has begun to work with some complex structures— for example, with agenda setting— but has a very limited empirical tradition. In contrast, most of the methods being developed in AI/IR are
general: For example, the methods of analyzing informal rules in the context of bureaucracies or international regimes should be of substantial use to those studying formal institutions such as Congress or the evolution of international regimes. The question of what will be tested remains open. In general there are two possible criteria: outcome validity and process validity. A model with outcome validity would reproduce or predict behavior but make no presumptions that the mechanisms through which that behavior was generated corresponded to those in the international system. The chapters using standard IR data sets (e.g., Hudson and Schrodt) are most clearly in this camp. A stricter, and more useful, criterion is process validity: The mechanisms by which an outcome is reached should also correspond to the observed in the actual system. The models strongly based in archival and primary source material (e.g., JESSE; Sylvan, Milliken, and Majeski) a coming closer to achieving this. A problem in testing any model of process validity, however, is the arbitrariness and incompleteness of the empirical record: A model may produce behavior and rationales that are entirely plausible (as evaluated by experts) but that are not found in the actu historical record.
Conclusion Despite the tendency at paradigm proliferation in the IR literature —two articles (at most) seem to establish a new IR paradigm— the AI/IR literature has most of the characteristics of a classical Kuhnian paradigm, including a new set of questions, theories of data, and techniques. As a paradigm, AI/IR allows one to look at old data in new ways; one could argue that it also arose out of failures of the behaviorialist modeling techniques to deal with the contextual complexity found in primary source material. The projects reported in this volume are in various stages of completion. Although the "trust me" assurances in research are quite rightly viewed with skepticism in a new and intensely hyped technique such as AI, most of this research has a large inductive component based in extensive primary source material rather
than data sets from the ICPSR and justifiably proceeds slowly. The completed research, frequently of considerable complexity (e.g., Hudson, the JESSE simulation, Job and Johnson), is, I hope, a harbinger for the results of the projects still in progress (e.g., Ensign and Phillips). Its vocabulary is evolving and its critical concepts emergent, yet the quantity and diversity of the AI/IR research projects have already transcended those of most faddish modeling techniques. The research reported in this volume opens a variety of doors to further research, and the list of potentially interesting projects is far from exhausted.
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