Detecting United States Mediation Styles in the Middle East, 1979-1998

This research is part of the "Multiple Paths to Knowledge Project" sponsored by the James A. Baker III Institute for Public Policy, Rice University, and the Program in Foreign Policy Decision Making, Texas A&M University. The paper deals with the problem of determining whether the mediation styles used by four U.S. Secretaries of State—George Schultz, James Baker, Warren Christopher and Madeline Albright—are sufficiently distinct that they can be detected in event data. The mediation domain is the Israel-Palestinian conflict from April 1979 to December 1998, the event data are coded from the Reuters news service reports using the WEIS event coding scheme, and the classification technique is hidden Markov models. The models are estimated for each of the four Secretaries based on 16 randomly chosen 32events sequences of USA>ISR and USA>PAL events during the term of the Secretary. Each month in the data set is then assigned to one of the four Secretarial styles based on the bestfitting model. The models differentiate the mediation styles quite distinctly and this method of detecting styles yields quite different results when applied to ISR-PAL data or random data. The "Baker" and "Albright" styles are most distinctive; the "Schultz" style is least; both results are consistent with many qualitative characterizations of these periods. A series of t-tests is then done on Goldstein-scaled scores to determine whether the mediation styles translate into statistically distinct interactions in the ISR>USA, ISR>PAL, PAL>USA and PAL>ISR dyads. While there are a number of statistically-significant differences when the full sample is used, these may be due simply to the overall changes Israel-Palestinian relations over the course of the time series. When tests are done on months that are out-ofterm—in other words, where the style of one Secretary is being employed during the term of another—few statistically-significant differences are found, though there is some indication of a lag of a month or so between the change in style and the behavioral response. It appears that the effects of the differing styles are not captured by changes in aggregated data, possibly because these scales force behavior into a single conflict-cooperation dimension. (In keeping with the protocols of the "Multiple Paths to Knowledge" conference, I have placed into the text of the paper an assortment of pedagogical notes concerning the process of constructing it.) This paper grew out of a collaboration that came together during the Rice meeting. Jonathan Wilkenfeld (Maryland), Charles Taber (SUNY Stony Brook) and I were assigned to a group, and realized that our research methods—experimental methods, artificial intelligence and event data respectively—presented a natural hierarchy of control. I could look at the problem of mediation using event data covering a real-world problem: with this approach, I had no controls on the data, and simply had to take what the historical record gave me. Wilkenfeld could use a formal experimental design, where he could control some aspects of the situation (notably the tactics used by the mediator and the presentation of a problem, in his case one abstracted from a Canada-Spain fisheries dispute) and measure others (notably the "cognitive complexity" of his subjects) but he could not actually find out why the subjects were behaving as they did. Finally, Taber's artificial intelligence simulations would give him complete control of every aspect of the situation, including the problem-solving methods used by the simulated actors. The three of us were generally acquainted with each other's work—and Taber and I had been on a number of panels together over the years—but we had never actually worked together. We did, however, share a common dissertation advisor—Dina Zinnes—which might have contributed to a common outlook on how one should do research. The coordination between the projects was very loose, with occasional exchanges of email and a few decisions on common vocabulary. This research is part of the "Multiple Paths to Knowledge Project" sponsored by the James A. Baker III Institute for Public Policy, Rice University, and the Program in Foreign Policy Decision Making, Texas A&M University. This paper deals with the problem of determining whether the mediation styles used by four U.S. Secretaries of State—George Schultz, James Baker, Warren Christopher and Madeline Albright—are sufficiently distinct that they can be detected in event data, and whether the different mediation styles have different results on the course of the conflict. The mediation domain is the Israel-Palestinian conflict from April 1979 to December 1998, the event data are coded from the Reuters news service reports using the WEIS coding system, and the modeling technique is hidden Markov models. Why this data set and model? Simple: the data were readily available and I figured the model would work. I had been working with the Levant data set for a number of years—as well as spending considerable time in the region itself—and was both comfortable with the data and confident of its quality. The Israeli-Palestinian conflict is highly conspicuous and most U.S. Secretaries of State try to put their individual stamp on mediating the conflict, so I anticipated that I could find distinctive styles. The original paper proposal, reflected in the title appearing in the International Studies Associations program, had a parallel analysis with data from the on-going crisis in the former Yugoslavia. I encountered some problems obtaining this data and wasn't able to incorporate it, which weakened the overall analysis. Since 1967, the Israeli-Palestinian dispute has been one of the key foreign policy foci of the United States. Despite this region being almost totally insignificant on most traditional measures geopolitical importance—its total population and area are smaller than that of many metropolitan areas, and it controls no vital trade routes or resources1—its symbolic importance places the conflict high on the priority list of U.S. Secretaries of State. Warren Christopher, for example, made some twenty major trips to the Middle East and only one to China. From the "shuttle diplomacy" of Henry Kissinger to the Camp David Accords of Jimmy Carter through the recently-concluded 1998 Wye River Agreement, Middle East diplomacy has played a major role in virtually every administration over the past 30 years. The assumption of this analysis is that the various U.S. Secretaries of State have had distinctive styles in attempting to mediate the Israeli-Palestinian dispute. At a qualitative level, this assumption is relatively uncontroversial (see for example Gerner 1991; Quandt 1993; Tessler 1994; Eisenberg & Caplan 1998; and Guyatt 1998). The purpose of this paper is to ascertain whether those styles can be systematically characterized using international event data and what, if any, difference these styles make in the resolution of the conflict. Hidden Markov models2 Techniques for analyzing sequences of discrete events—nominal-level variables occurring over time—are poorly developed compared to the huge literature involving the study of intervallevel time series. Nonetheless, several methods are available, and the problem has received considerable attention in the past three decades because it is important in the problems of 1 On the eve of the first elections to the Palestinian National Council in January 1995, Albert Aghazarian, the public-relations director of Birzeit University, began his remarks—to an East Jerusalem hotel ballroom crowded with the international media—by observing, "Let me say that I am pleased that so many of you have travelled thousands of miles and spent many tens of thousands of dollars to observe what is essentially a municipal election. " 2This description is shamelessly lifted from Schrodt (1999) with modifications. studying genetic sequences in DNA, and computer applications in involving human speech recognition. (Both of these problems have potentially large economic payoffs, which tends to correlate with the expenditure of research efforts.) Hidden Markov models (HMM) are a recently developed technique that is now widely used in the classification of noisy sequences into a set of discrete categories (or, equivalently, computing the probability that a given sequence was generated by a known general model). A sequence is "noisy" when it contains missing, erroneous and extraneous elements, and consequently the sequence cannot be classified by simply matching it to a set of known "correct" sequences. A spell checking program, for example, would always mark "wan" as an incorrect spelling of "one", because written English usually allows one and only one correct spelling of a word. Spoken English, in contrast, allows a wide variation of pronunciations, and in some regional dialects, "wan" is the most common pronunciation of "one". A computer program attempting to decipher spoken English needs to provide for a variety of different ways that a word might be pronounced, whereas a spelling checker needs only to know one. While the most common applications of HMMs are found in speech recognition and comparing protein sequences, a recent search of the World Wide Web found applications in fields as divergent as modeling the control of cellular phone networks, computer recognition of American Sign Language and—inevitably—the timing of trading in financial markets. The standard reference on HMMs is Rabiner (1989), which contains a thorough discussion of the estimation techniques used with the models as well as a notation that is used in virtually all contemporary articles on the subject. The hidden Markov model, like the data, was readily available. However, getting to that model had been a long process with numerous false starts. I had been searching since about 1984 (when I began working with computational modeling and artificial intelligence te