Hostname: page-component-699b5d5946-62fq4 Total loading time: 0 Render date: 2026-03-08T11:54:27.676Z Has data issue: false hasContentIssue false
  • English
  • Français

On prediction in political science

Published online by Cambridge University Press:  01 January 2026

Keith Dowding  and
Charles Miller
Keith Dowding
Affiliation:
Australian National University, Australia
Charles Miller
Affiliation:
Australian National University, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

This article discusses recent moves in political science that emphasise predicting future events rather than theoretically explaining past ones or understanding empirical generalisations. Two types of prediction are defined: pragmatic, and scientific. The main aim of political science is explanation, which requires scientific prediction. Scientific prediction does not necessarily entail pragmatic prediction nor does it necessarily refer to the future, though both are desiderata for political science. Pragmatic prediction is not necessarily explanatory, and emphasising pragmatic prediction will lead to disappointment, as it will not always help in understanding how to intervene to change future outcomes, and policy makers are likely to be disappointed by its time‐scale.

Keywords

explanationinternational relationsphilosophy of social sciencepolitical scienceprediction

Information

Type
Original Articles
Information
European Journal of Political Research , Volume 58 , Issue 3 , August 2019 , pp. 1001 - 1018
Copyright
Copyright © 2019 European Consortium for Political Research

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Axelrod, R. (1984). The evolution of co‐operation. London: Penguin.Google Scholar
Beger, A. & Ward, D. (2017). Lessons from near real‐time forecasting of irregular leadership changes. Journal of Peace Research 54(2): 141‒156.Google Scholar
Binmore, K. (2007). Playing for real: A text on game theory. Oxford: Oxford University Press.CrossRefGoogle Scholar
Boyd, R. (1984). The current status of scientific realism. In Leplin, J. (ed.), Scientific realism. Berkeley, CA: University of California Press.Google Scholar
Bueno de Mesquita, B. (2010). The predictioneer's game: Using the logic of brazen self‐interest to see and shape the future. New York: Random House.Google Scholar
Bueno de Mesquita, B., Smith, A., Siverson, R.M. & Morrow, J.D. (2005). The logic of political survival. Cambridge, MA: MIT Press.Google Scholar
Calvert, R.L. (1995a). Rational actors, equilibrium and social institutions. In Knight, J. & Sened, I. (eds), Explaining social institutions. Ann Arbor, MI: University of Michigan Press.Google Scholar
Calvert, R.L. (1995b). The rational choice theory of social institutions: Cooperation, coordination and communication. In Banks, J.S. & Hanushek, E.A. (eds), Modern political economy. Cambridge: Cambridge University Press.Google Scholar
Campbell, J.E. et al. (2016). Symposium: Forecasting the 2016 American national election. Political Science and Politics 49(4): 649‒690.Google Scholar
Carnap, R. (1966). Philosophical foundations of physics: An introduction to the philosophy of science. New York: Basic Books.Google Scholar
Clarke, K.A. & Primo, D.M. (2012). A model discipline: Political science and the logic of representations. Oxford: Oxford University Press.CrossRefGoogle Scholar
Dawid, R. (2013). String theory and the scientific method. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
De Marchi, S., Gelpi, C. & Grynaviski, J.D. (2004). Untangling neural nets. American Political Science Review 98(2): 371‒378.CrossRefGoogle Scholar
Dennett, D.C. (1998). Real patterns. In Brainchildren: Essays on designing minds. Harmondsworth: Penguin.CrossRefGoogle Scholar
Dowding, K. (2016). The philosophy and methods of political science. Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
Dowding, K. (2017). So much to say: Response to commentators. Political Studies Review 15(2): 217‒230.CrossRefGoogle Scholar
Druckman, J.N. et al. (2011). Cambridge handbook of experimental political science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Fearon, J.D. & Laitin, D.D. (2003). Ethnicity, insurgency and civil war. American Political Science Review 97(1): 75‒90.CrossRefGoogle Scholar
Godfrey‐Smith, P. (2003). Theory and reality: An introduction to the philosophy of science. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Goldsmith, B.E. et al. (2013). Forecasting the onset of genocide and politicide: Annual out‐of‐sample forecasts on a global dataset, 1988‒2003. Journal of Peace Research 50(4): 437‒452.CrossRefGoogle Scholar
Goldstone, J.A. et al. (2010). A global model for forecasting political instability. American Journal of Political Science 54(1): 190‒208.CrossRefGoogle Scholar
Harré, R. (1986). Varieties of realism. Oxford: Blackwell.Google Scholar
Head, M. et al. (2015). The extent and consequences of p‐hacking in science. PLoS Biology 13(3): e1002106.CrossRefGoogle ScholarPubMed
Hegre, H. et al. (2013). Predicting armed conflict, 2010–2050. International Studies Quarterly 57(2): 250‒270.Google Scholar
Hempel, C. (1942). The function of general laws in history. Journal of Philosophy 39(1): 35–48.CrossRefGoogle Scholar
Hindman, M. (2015). Building better models: Prediction, replication and machine learning in the social sciences. Annals of the American Academy of Political and Social Science 659(1): 48‒62.Google Scholar
Hitchcock, C. & Sober, E. (2004). Prediction versus accommodation and the risk of overfitting. British Journal for the Philosophy of Science 55(1): 1‒34.CrossRefGoogle Scholar
Jäger, K. (2016). Not a new gold standard: Even big data cannot predict the future. Critical Review 28(3‒4): 335‒355.CrossRefGoogle Scholar
Johnson, J. (2017). Models‐as‐fables: An Alternative to the Standard Rationale for Using Formal Models in Political Science. Paper presented at Midwest Political Science Association, Chicago.Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus & Giroux.Google Scholar
King, G., Keohane, R. & Verba, S. (1994). Designing social inquiry. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Ladyman, J. et al. (2007). Every thing must go. Oxford: Oxford University Press.CrossRefGoogle Scholar
Lantz, B. (2013). Machine learning with R. Birmingham, AL: Packt.Google Scholar
Lipton, P. (1991). Inference to the best explanation. London: Routledge.CrossRefGoogle Scholar
Montgomery, J. & Nyhan, B. (2010). Bayesian model averaging: Theoretical developments and practical applications. Political Analysis 18(2): 245‒270.CrossRefGoogle Scholar
Morton, R. & Williams, K. (2010). Experimental political science and the study of causality: From nature to the lab. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mullainathan, S. & Spiess, J. (2017). Machine learning: An applied econometric approah. Journal of Economic Perspectives 312(2): 87‒106.Google Scholar
Nanlohy, S., Butcher, C. & Goldsmith, B.E. (2017). The policy value of quantitative atrocity forecasting models. RUSI Journal 162(2): 24.CrossRefGoogle Scholar
Popper, K. (1972). The logic of scientific discovery, rev. edn. London: Hutchinson.Google Scholar
Popper, K. (1974). Replies to my critics. In Schilpp, P.A. (ed.), The philosophy of Karl Popper: Library of living philosophers. La Salle, IL: Open Court.Google Scholar
Popper, K. (1983). Realism and the aim of science. London: Routledge.Google Scholar
Popper, K. (1989). Conjectures and refutations: The growth of scientific knowledge, rev. edn. London: Routledge.Google Scholar
Riker, W.H. (1982). Liberalism against populism: A confrontation between the theory of democracy and the theory of social choice. San Francisco, CA: WH Freeman & Co.Google Scholar
Ross, D. (2014). Philosophy of economics. Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
Sala‐i‐Martin, X. (1997). I just ran two million regressions. American Economic Review 87(2): 178–183.Google Scholar
Schrodt, P. (2014). Seven deadly sins of contemporary quantitative political science. Journal of Peace Research 51(2): 287‒300.CrossRefGoogle Scholar
Schultz, K.A. (2001). Looking for audience costs. Journal of Conflict Resolution 45(1): 32‒60.CrossRefGoogle Scholar
Schwartz, G. (1978). Estimating the dimensions of a model. Annals of Statistics 6(2): 461‒465.Google Scholar
Shepsle, K.A. (1979). Institutional arrangements and equilibrium in multidimensional voting models. American Journal of Political Science 23(1): 27‒59.CrossRefGoogle Scholar
Silver, N. (2012). The signal and the noise: Why so many expert predictions fail – but some donʼt. New York: Penguin Press.Google Scholar
Tetlock, P. (2005). Expert political judgment: How good is it? How can we know? Princeton, NJ: Princeton University Press.Google Scholar
Tetlock, P. & Gardner, D. (2016). Superforecasting: The art and science of prediction. London: Random House.Google Scholar
Tsebelis, G. (2002). Veto players: How political instutions work. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Ulfelder, J. (2012). The watch list: Predicting state failure isnʼt as hard as you think. Foreign Policy, 18 June. Available online at: http://foreignpolicy.com/2012/06/18/the-watch-list/Google Scholar
Ulfelder, J. (2014). Coup forecasts for 2014. Available online at: https://dartthrowingchimp.wordpress.com/2014/01/25/coup-forecasts-for-2014/Google Scholar
Ulfelder, J. (2015). Statistical assessment of coup risk for 2015. Available online at: https://dartthrowingchimp.wordpress.com/2015/01/17/statistical-assessments-of-coup-risk-for-2015/Google Scholar
Waltz, K. (1979). Theory of international politics. Boston, MA: McGraw‐Hill.Google Scholar
Ward, M.D. (2016). Can we predict politics? Toward what end? Journal of Global Security Studies 1(1): 80–91.CrossRefGoogle Scholar
Ward, M.D., Greenhill, B.D. & Bakke, K.M. (2010). The perils of policy by p‐value: Predicting civil conflicts. Journal of Peace Research 47(4), 363‒375.CrossRefGoogle Scholar
Ward, M.D. et al. (2013). Learning from the past and stepping into the future: Toward a new generation of conflict prediction. International Studies Review 15(4): 473‒490.CrossRefGoogle Scholar
White, R. (2003). The epistemic advantage of prediction over accommodation. Mind 112(448): 653‒683.CrossRefGoogle Scholar
Williamson, T. (2007). The philosophy of philosophy. Oxford: Blackwell.CrossRefGoogle Scholar
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.Google Scholar
Yarkoni, T. & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science 12(6): 1100‒1122.CrossRefGoogle ScholarPubMed