Dina A. Zinnes

The Dynamics of Hostile Activity and the Prediction of War

In an earlier article (Muncaster and Zinnes, 1983) a dynamic model was presented that linked hostile interaction, protracted conflict, and war. The key variables in the model included interaction, grievance, fear of war, and a pull to war. The present article extends the first model by including the amount of hostile activity. Given this additional factor it is possible to predict not only the time at which a war will occur, but also the level of hostility in the system necessary for war to occur. The model further indicates why some hostile interactions cycle in extended protracted conflict patterns while others shoot off to war.

International data as a national resource

Nation‐states are increasingly recognizing that banks of knowledge, including social science data, are a national resource vital for understanding the course of events. Criteria for considering a dataset in the fields of cross‐national and interstate politics to be a national resource include theoretic fertility, centrality in current research, cumulative contribution to knowledge, data quality, and methodological contribution. Published data‐based research and responses to a questionnaire by leading North American researchers in the field indicate substantial consensus on the importance of maintaining and improving datasets on national attributes (such as the World Handbooks) and interstate conflict (especially the Correlates of War projects). Researchers should also pay attention to rethinking and generating enhanced datasets of both interstate events and the international political economy.

Constructing Political Logic The Democratic Peace Puzzle

The statistical observation that war between democratic states is exceptionally rare and the seemingly contradictory finding that democracies nevertheless do go to war have posedan intriguing puzzle for the field of international politics. The two explanations that have gained the greatest currency, the normative and structural, have commanded center stage for nearly a decade. Recently, however, these explanations have come under attack. Using the propositional calculus, this study provides a logical construction of both arguments that (1)explains the empirical results of why two democracies do not goto war whereas all other dyads do go to war, (2) compares the normative and structural theories, and (3) facilitates an assessment of the critiques leveled at both theories.

A Model of Inter-Nation Hostility Dynamics and War

There are two traditions of research in the field of international relations that have had surprisingly little to say to one another. On the one hand, there have been a number of studies of the dynamics of hostile interaction prior to and during major crises. Wilkenfeld, et al. (1972), Burrowes and Garriga-Pic0 (1974), Azar, et al. (1974) and Zinnes, et al. (1982) have proposed and tested models that account for hostile interactions between pairs of nations over time. On the other hand, there have been numerous studies under the general rubric of “the correlates of war” that have attempted to assess various national and systemic attributes that might predict the outbreak of war, e.g., Singer and Small (1968), Singer, et al. (1972), Wallace (1973), Bueno de Mesquita (1978). But while there are obvious relationships between the growth of hostile interactions and the prospects for war, there have been no studies that link these two literatures. The dynamics of interactive hostility has not been used so far to explicity forecast war. The present paper will develop and explore a model of hostile interactions that will make this connection. The model provides a qualitative description of the dynamics of hostility behavior and allows us to say something, within the confines of this single model, about protracted conflict, approach to war, and the predicition of time of war. While an empirical test of the model is only now under investigation, we indicate how such analyses might proceed.

Validity of power indices

An important approach to understanding power seeks a generalized measure that can rank nation‐states from the most to the least powerful. Data‐oriented researchers have developed several such single‐ and multivariable, linear and nonlinear indices. The test of their usefulness varies with their intended purpose. For policymakers desiring the most efficient way to assess the likelihood that a nation‐state can achieve its goals, the key issues are parsimony in the selection of variables, stability over time, combinatorial patterns enabling successful prediction, and applicability across a range of situations. For theoreticians the index must “work” within the chosen theoretical context. If several indices meet the constraints imposed by the theory, then that one should be chosen that introduces the least amount of additonal (nontheoretical) information.

Structure and Hostility in International Systems

This article presents a comprehensive theoretical classification of the patterns of hostile behaviour of an international system based upon a mathematical model of conflict processes. The model provides for multifaceted behaviour, including combinations of progress to war, protracted conflict and conflict resolution. It is postulated that the hostile behaviour among nation-states is determined by a system of social forces that captures effects of grievance, friendship, fear, aggression, deterrence and a pull to war. The possible relationship between social force configurations and conventional structure variables, such as alliances, polarity and power distribution, is also discussed.

Parameter estimation by numerical minimization methods

The purpose of this paper is to discuss a technique which can be used to solve estimation problems for which the more commonly accepted analytical methods such as linear regression analysis are inappropriate. The estimation method proposed consists of using computer alogrithms for numerical minimization of functions. The basic method of functional minimization is discussed and examples are provided. Various measures of fit for non‐unique solutions, and statistical tests on the parameter estimates are addressed. The paper concludes that functional minimization is not a panacea for all parameter estimation problems, but for many models can provide parameter estimates and statistical tests which otherwise would not be available.

The war propensity of international systems

The conjecture that international system structure determines war propensity has met with mixed results in past theory in political science. This question is reexamined within the context of a dynamic model of inter-nation hostile behavior. System structure is defined in terms of the degrees of grievance, fear, etc., among nations and also in terms of the qualitative patterns of hostile behavior that are possible. Propensity for war is measured in terms of the likelihood of progress to war within a given class of hostile behaviors. Then the dynamic model is used to analyze in detail and interpret the relationship between system structure and war propensity.

Data development for international research

The Data Development for International Research (DDIR) project seeks to maintain, extend, and develop major data banks for the study and analysis of cross‐national and international political phenomena. It is the outgrowth of three years of discussions, correspondence, and seminars involving major researchers, both data collectors and data users, across the United States. Funding for 1986–89 by the National Science Foundation enables DDIR to focus on two tasks: (1) A dozen subprojects at seven universities will update, extend, and coordinate data‐collection efforts in two key areas of research, national attributes and international conflict; and (2) through a series of conferences, DDIR will explore appropriate methodologies, sources, and procedures for future data‐collection efforts in the areas of inter‐nation events and international political economy.

Reflections of a Past Editor

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