Heuristics have rapidly become a core concept in the study of political behavior. The term heuristic stems from the ancient Greek heuriskein, which means “to discover.” In psychology and political science, the term is used to describe cognitive shortcuts in decision making under uncertainty. The key idea is that decision makers with limited time, information, or resources use such shortcuts, thereby bypassing a certain amount of information to reach appropriate decisions. In this sense, heuristics contrast with classical rational choice. Using heuristics allows efficient decision making but can lead to biases, errors, and suboptimal decisions. Heuristics allow decision makers to draw inferences, to fill in information gaps, and to form an impression of the decision at hand. Indeed, they may be the only way to come to grips with uncertainty, especially when a decision is urgent. In political science, the concept of heuristics, originating in mathematics, economics, and psychology, has long been hailed as a possible remedy to citizens’ lack of political knowledge. Citizens participate in democratic decisions, but these decisions often pose high cognitive and informational demands. Ideally, citizens with little information about a political issue or about a candidate could use heuristics in order to reach decisions resembling those of their more well-informed peers. More recently, however, the possible biases introduced by reliance on heuristics, in particular partisan bias and a lack of consideration of different alternatives, has received more attention. Moreover, some studies show that heuristics can be used most efficiently by voters who are relatively well informed and highly interested in politics. The question of whether, or under which circumstances, heuristics can be a useful tool for democratic decision making has not yet been answered conclusively.
Celine Colombo and Marco R. Steenbergen
It can be difficult for political scientists and economists to know when to use laboratory experiments in their research programs. There are longstanding concerns in economics and political science about the external invalidity of laboratory results. Making matters worse, a number of prominent academics recommend using field experiments instead of laboratory experiments to learn about human behavior because field experiments do not have the same external invalidity problems that plague laboratory experiments. The criticisms of laboratory experiments as externally invalid, however, overlook the many advantages of laboratory experiments that derive from their external invalidity. Laboratory experiments are preferable to field experiments at examining hypothetical scenarios (e.g., When automated vehicles dominate the roadways, what principles do people want their automobiles to rely on?), at minimizing erroneous causal inferences (e.g., Did a treatment produce the reaction researchers are studying?), and at replicating and extending previous studies. Rather than being a technique that should be abandoned in favor of field experiments, political scientists and economists should embrace laboratory experiments when testing theoretically important but empirically unusual scenarios, tracing experimental processes, and reproducing and building on prior experiments.
Like all decision making, foreign policy decision making (FPDM) requires transferring meaning from one representation to another. Since the end of the Cold War, students of FPDM have focused increasingly on historical analogies and, to a lesser extent, conceptual metaphors to explain how this transference works. Drawing on converging evidence from the cognitive sciences, as well as careful case studies of foreign policymaking, they’ve shown analogy and metaphor to be much more than “cheap talk.” Instead, metaphor and analogy are intrinsic to policymakers’ cognition. This article traces the development of this growing literature. So far, FPDM has treated analogy and metaphor separately. It has also paid far more attention to the former than the latter. By contrast, the article argues that analogy and metaphor are not only similar, they are equally essential to cognition. It defines and compares metaphor and analogy, analyzes their socio-cognitive functions in decision making, and charts the evolution of analogy and metaphor research in FPDM. It also suggests the utility of a constructivist-cognitive synthesis for future work in this area.
Tomasz Warczok and Tomasz Zarycki
Looking at the contemporary Polish political sciences in a wider international perspective—and specifically analyzing their location within the global hierarchies of academic knowledge production—may not only shed new light on the field but will also provide interesting insight to workings of social sciences in peripheral context. The position of the Polish political science, as measured in terms of international rankings or indexes of citations, is rather low. Moreover, its dominant intellectual schools and most commonly used methodological approaches may be considered old fashioned from the perspective of leading Western centers of the discipline. Descriptive analysis and traditional institutionalism dominate, while more sophisticated behavioral approaches or new institutionalism are rare. On the other hand, the field as such may be seen as quite strong, especially given its visibility in the national media, its considerable institutional and human resources, or high numbers of students attracted each year. Moreover, it can be argued that the field has achieved considerable autonomy from the global political science system and has successfully endured post-Communist transformation, retaining most of its staff and institutional assets from the previous regime (which was not the case in most other central European countries). At the same time, one can find within it a smaller faction of internationally oriented scholars. They contest the dominant, locally oriented majority of the field and are well connected to global academic networks. In effect, an interesting duality within Polish political science may be observed and interpreted as a phenomenon typical for many peripheral countries, understood in terms of the world systems theory. Relying on Wallersteinian perspective on the global system of social sciences coupled with Bourdieusian field analysis allows for reconstruction of the genesis and underlying structures of the contemporary field of political sciences in Poland, which may be interpreted as a case of successful autonomy building of a peripheral field of social sciences.
The crackdown on Falun Gong by the Chinese Communist Party demonstrates the unintended consequences of the deep penetration of politics into religious affairs in an authoritarian regime. Falun Gong emerged in China in the early 1990s as a state-sanctioned health practice, or qigong. Initially it focused on treating physical diseases and promoting general health, and therefore received recognition from the state, which has granted legal status to only the five institutional religions while relentlessly suppressing secret religious societies. Qigong, however, has contained spiritual elements since its inception. In the mid-1990s, Falun Gong began to reveal and highlight its spiritual teachings. While this differentiation strategy brought it a huge following, it sent alarming signals to the ruling Communist Party. As the state sought to curb its influences, Falun Gong responded with open defiance. In particular, its tenets of truthfulness, compassion, and forbearance encouraged the practitioners to launch a “truth clarification” campaign, targeting local political authorities and media outlets. The campaign achieved moderate initial success, but Falun Gong’s persistent and coordinated efforts to demonstrate its “apolitical” nature convinced the state that it was indeed a politically subversive force. Falun Gong’s political defiance culminated in a large, 13-hour sit-in protest near the central government compound in Beijing. Three months later, the state officially banned Falun Gong and mobilized its entire security and propaganda apparatus to eliminate Falun Gong in China.
Kumail Wasif and Jeff Gill
Bayes’ theorem is a relatively simple equation but one of the most important mathematical principles discovered. It is a formalization of a basic cognitive process: updating expectations as new information is obtained. It was derived from the laws of conditional probability by Reverend Thomas Bayes and published posthumously in 1763. In the 21st century, it is used in academic fields ranging from computer science to social science. The theorem’s most prominent use is in statistical inference. In this regard, there are three essential tenets of Bayesian thought that distinguish it from standard approaches. First, any quantity that is not known as an absolute fact is treated probabilistically, meaning that a numerical probability or a probability distribution is assigned. Second, research questions and designs are based on prior knowledge and expressed as prior distributions. Finally, these prior distributions are updated by conditioning on new data through the use of Bayes’ theorem to create a posterior distribution that is a compromise between prior and data knowledge. This approach has a number of advantages, especially in social science. First, it gives researchers the probability of observing the parameter given the data, which is the inverse of the results from frequentist inference and more appropriate for social scientific data and parameters. Second, Bayesian approaches excel at estimating parameters for complex data structures and functional forms, and provide more information about these parameters compared to standard approaches. This is possible due to stochastic simulation techniques called Markov Chain Monte Carlo. Third, Bayesian approaches allow for the explicit incorporation of previous estimates through the use of the prior distribution. This provides a formal mechanism for incorporating previous estimates and a means of comparing potential results. Bayes’ theorem is also used in machine learning, which is a subset of computer science that focuses on algorithms that learn from data to make predictions. One such algorithm is the Naive Bayes Classifier, which uses Bayes’ theorem to classify objects such as documents based on prior relationships. Bayesian networks can be seen as a complicated version of the Naive Classifier that maps, estimates, and predicts relationships in a network. It is useful for more complicated prediction problems. Lastly, the theorem has even been used by qualitative social scientists as a formal mechanism for stating and evaluating beliefs and updating knowledge.