COMPARED TO CONTEMPORARY POLITICAL SCIENCE, PHYSICS AIMS AT BEING QUANTITIVE RATHER THAN EXACT, QUOTES FACTS RATHER THAN AUTHORITIES, IS ON THE LOOKOUT FOR UNIVERSAL LAWS THAN ANALYSIS. THIS ARTICLE INTRODUCES INTO POLITICAL SCIENCE THE USE OF ALGEBRAIC MODELS RATHER THAN STATISTICS AND CALCULATIONS RATHER THAN PHILOSOPHY AND THEORETICAL METHODOLOGY.
Science walks on two legs. One leg consists of asking: How things are? This leads to observation, measurement, graphing, and statistical description. The other leg consists of asking: How things should be, on logical grounds? This leads to logical models that should become quantitatively predictive. Science largely consists of such models, tested with data. Developed science establishes not only connections among individual factors but also connections among these connections. As an illustration, I use laws about human activity I have found. But social sciences often take the lazy road of fitting raw data with a straight line or some fashionable format, unaware of the need to think and build models based on logic, as stressed by Karl Deutsch. As expounded in my Making Social Sciences More Scientific (2008) and Logical Models and Basic Numeracy in Social Sciences, www.psych.ut.ee/stk/Beginners_Logical_Models.pdf, I call for a major widening in social science methodology.
The Nagayama triangle represents the conceptually allowed area when the vote or seat shares of the second-running contestant are graphed against the shares of the top contestant. This research note points to various uses of this tool in the study of Duvergerian processes.
Lijphart's (1999) analysis maps countries along two dimensions of democratic institutions: 'executives-parties' or 'joint-power', and 'federal-unitary' or 'divided-power'. My 'meta-study' maps the methodology of Lijphart's mapping: the nature of indices (inputs or outputs), their logical interconnections, their susceptibility to institutional design ('constitutional engineering'), and their suitability for expressing the intended underlying concepts. Strikingly, the joint-power indicators are highly correlated and mostly logically connected output measures, which are not susceptible to institutional design, while the opposite is true for the divided-power dimension. For this dimension most indices are expert estimates of inputs, marginally correlated, yet subject to institutional design, limited by size dependence. Surprisingly, the parliamentary-presidential aspect of institutional design does not affect the picture. The connection between cabinet life and the number of parties is even stronger than found by Lijphart. Interest groups and central bank independence fit his dimensions empirically but less so logically. In sum, institutional design may be more difficult than sometimes assumed, but offers hope.
Lijphart's (1999) analysis maps countries along two dimensions of democratic institutions: 'executives-parties' or 'joint-power', and 'federal-unitary' or 'divided-power'. My 'meta-study' maps the methodology of Lijphart's mapping: the nature of indices (inputs or outputs), their logical interconnections, their susceptibility to institutional design ('constitutional engineering'), and their suitability for expressing the intended underlying concepts. Strikingly, the joint-power indicators are highly correlated and mostly logically connected output measures, which are not susceptible to institutional design, while the opposite is true for the divided-power dimension. For this dimension most indices are expert estimates of inputs, marginally correlated, yet subject to institutional design, limited by size dependence. Surprisingly, the parliamentary-presidential aspect of institutional design does not affect the picture. The connection between cabinet life and the number of parties is even stronger than found by Lijphart. Interest groups and central bank independence fit his dimensions empirically but less so logically. In sum, institutional design may be more difficult than sometimes assumed, but offers hope.