Historical institutionalism

AI is a fairly young scientific field, however, a methodology with an emphasis on history was used much earlier than its immediate origin.

The emergence of AI occurred in the late 1970s - 1980s. As S. Steinmo notes ^[2] :

“By the 1960-1970s. sociology was divided into two distant directions: on the one hand, micro-analysis of political behavior that is not based on theory, and on the other, macro-theory (Marxism, functionalism, system theory, rational choice theory, etc.), which there was empirical evidence. [...] Without necessarily denying the goals of sociology, many researchers continued to be interested in meso-analysis and mid-level theories. Disappointed by comprehensive theories and disinterested in the purely technical approach of behaviorism, sociologists continued to attract events taking place in the real world. This is where historical institutionalism arose. ”

Original text

By the 1960s and 1970s, social science's cutting edges had moved in quite distinct directions: the largely a-theoretical micro-analyses of political behavior on the one hand; and the macro- (and remarkably non-empirical) theorizing of Marxism, Functionalism, Systems Theory and Rational Choice on the other. [...] Without necessarily denying the goal of social science qua science, many continued to be interested in the meso-level analysis and middle-ranged theory. Dissapointed with grand theory and bored or simply uninsterested in the technical approach of behaviouralism, many political scientists continued to be interested in real world outcomes. It was here that historical institutionalism was born.

The answer to the trends described by S. Steinmo was the emergence of comparative studies in which the authors examined the influence of the historical context on the further development of events. Among them, one can note the States and Social Revolutions ( T. Skochpol ), Between Power and Plenty ( P. Katzenstein ), and others. The final design of AI as an independent direction occurred in the 1990s. One of the most influential works of this period was Institutions, Institutional Change, and Economic Performance ( D. North ) and Structuring Politics: Historical Institutionalism in Comparative Analysis (S. Steinmo, C. Thelen, F. Longstrett).

In the early stages of AI, researchers focused on the issues of how institutions influence player behavior, whereas in the 2000s. the emphasis began to shift towards institutional change. ^[3]

Temporality

Temporality is a fundamental concept in AI, which means that history is not a set of random events, but a chain in which each link is somehow interconnected with the others. Its main characteristics are:

1. Unpredictability - the consequences are almost impossible to predict;
2. Rigidity - the more time passes, the more difficult it is to change the selected course;
3. Inefficiency - missed alternatives may be more effective;
4. Non - ergodicity - random events have a lasting effect ^[1] .

History Effectiveness

Historical institutionalists criticize the idea of ​​“the effectiveness of history” ( J. G. March , J. Olsen ), according to which an effective historical process is “moving rapidly towards a unique solution that depends on the context and therefore does not depend from the historical path ” ^[4] . It is indicated that such a model does not take into account sunk costs (missed opportunities, alternatives, etc.) while they and other inheritance effects play a key role in the processes of development and evolution ^[1] .

Dependence on the chosen path

Another key element of historical institutionalism is the concept of dependence on the chosen path or the “track dependence ”. In relation to historical institutionalism, the “gauge effect” means that at a certain decisive moment the structure of the institute is fixed, making, thus, the appearance of alternatives, even more effective, less likely.

There are a number of reasons for the preservation of institutions over long periods of time ^[5] :

1. “Lock-in effect”. The adoption of norms and rules of behavior consolidates the balance of power and political paradigms and gives them a privileged position.
2. The effect of positive feedback (positive feedback effect). Following the rules of behavior enshrined in the institution creates positive externalities for individuals.
3. Increasing benefits (increasing returns). The benefits of reproducing the same social relationships increase over time.
4. Self-reinforcing Institutions have the ability to strengthen themselves, creating complementary relationships with each other and increasing the value of certain rules.

Unforeseen Consequences

By “unforeseen consequences” in AI is meant a situation in which an institute begins to develop in a completely different direction than originally implied. The main reasons for this may be the cognitive limitations of the creators of the institution or the complex interactions to which it is exposed throughout its existence. Supporters of AI, however, argue that unforeseen consequences may occur if the founders of the institute have complete information, act openly and are not limited in time. Moreover, they reject the inevitability of unforeseen consequences, since in most cases there are one-time political decisions that are not reproduced in time ^[1] .

Sequence of Events

For AI, the sequence of events plays an important role, since order determines the preferences of the players, as well as their ability to make decisions. Therefore, events are not isolated in time, but are analyzed based on the interests and contexts created by previous events.

Effect on player preferences

Unlike rational choice theory , which assumes that the player will always choose the most profitable alternative, AI pays special attention to point-to-point comparison and institutional context. The first means that when making a decision a player will evaluate not so much future benefits as what he will save or lose when adapting to new conditions ^[1] . The second implies that the decisions of the player will depend on the social and institutional environment ^[6] . Thus, his preferences will largely depend on previous decisions.

Concept of Institute in AI

In AI, an institution is defined as a combination of formal and informal rules of behavior that organize relationships between people. At the same time, institutions are usually separated from organizations. For example, D. North claims that one and the other regulate social relations, but if institutions approve the rules of the game, then “modeling of strategies and skills” takes place within organizations. At the same time, the influence of institutions and organizations on each other is mutual: organizations arise in an institutional framework and at the same time act as agents of institutional change ^[7] .

The nature of institutional change

In AI, there are two main approaches to institutional change ^[3] .

The first considers institutions as “equilibrium points,” which, once achieved, adapt to new conditions without changing their essence. The equilibrium can be disturbed by an external “exogenous shock”, after which the institute undergoes significant transformations. This approach has common features with the theory of discontinuous equilibrium , borrowed by S. Krasner from biology ^[8] .

According to the second approach, players play a more significant role in institutional change. Institutions are seen as “arenas of conflict” between those who set the rules (rule-makers) and those who want to adapt them to their interests and needs (rule-takers). Thus, institutional changes are not caused by external factors, but by internal dynamic components.

The role of formal and informal rules

An important role in the transformation of institutions is played by the interaction of formal and informal rules. The former are associated with laws and other officially enshrined norms ^[9] , while the latter include customs, traditions, moral norms, and so on ^[10] .

Historical institutionalists evaluate the relationship between formal and informal rules differently. According to one point of view, in order for laws to be effective, they should not contradict informal rules in society. According to another, formal rules, as a rule, are more progressive, while informal rules are seen as a kind of relics of the past and a burden, so they must be overcome ^[11] .

S. Peyovich identified four types of interactions between formal and informal rules ^[12] :

1. Formal rules suppress informal ones, but are not able to change them (anti-tobacco laws);
2. Formal and informal rules are in confrontation (prohibition of religious organizations);
3. Formal rules exist but are ignored;
4. Formal and informal rules interact (laws on the protection of property rights)

AI often reproaches that it claims to be a separate theory, without having a universal toolkit and conceptual apparatus. The inductive approach of historical institutionalists is also criticized, who, without outlining the general picture, investigate particular cases and try to derive general concepts from them. Advocates of the theory of rational choice in institutionalism note that such a methodology has no theoretical basis, and describe it as an ordinary “retelling of history” ^[6] .

In addition, B. G. Peters, J. Pierre, and D. S. King single out a number of AI problems. Firstly, he does not explain the process of adopting a particular political program. Secondly, AI researchers often overestimate the importance of officials and bureaucrats in decision-making. Thirdly, there is an exaggeration of the importance of institutions and an underestimation of the role of ideas. Fourth, the role of agents of change is poorly represented in AI. Finally, the main drawback of AI is the excessive emphasis on the “gauge effect”, which does not allow us to effectively describe institutional changes ^[13] .