The ICS research program is implemented in seven so-called research clusters. These clusters combine questions of solidarity, inequality and social networks in a specific societal domain, as shown below.


ICS Core Research Program

The school’s research program consists of a common field of interest, a common core, and a common research strategy. The common field of interest on which the program is focused are social dilemma’s or “level paradoxes”. Such paradoxes emerge because people strive for private goals, but need to contribute private resources for common ends. Strategies that are rational at the level of the individual can lead to unintended consequences or suboptimal outcomes at group or society level, thereby creating solidarity and inequality problems. Solidarity is broadly conceived as the contribution of private resources to common ends without direct compensation. Inequality is defined as unequal access to valued resources. Solidarity and inequality problems are studied in informal settings such as families, ethnic groups, and neighborhoods and in formal settings, such as relations within and between formal organizations. Social networks are important in maintaining or undermining solidarity and inequality. At the same time, solidarity and inequality (or their absence) feedback on the dynamics of social networks. In addition, problems of solidarity and inequality are intertwined. Depending on the conditions, inequality may either hamper or foster solidarity while, conversely, solidarity – in a relation between two actors or in groups – may have positive or negative effects for third parties or other groups, and may thus affect inequality. Networks comprise much of the social structure that generates, maintains, or undermines solidarity on the one hand and induces inequality on the other hand, so that questions of solidarity and inequality can be fruitfully studied by considering aspects of the social network between the actors.

The common core of the program consists in the integration of theory formation, methodologically advanced empirical research, and state of the art statistical modeling. The program conceives of social science as being problem- and theory-driven rather than data-driven. Theory construction aims at testable explanations of social phenomena rather than conceptual systems without explicit empirical content. Empirical analysis is theory-guided, aiming at deeper explanations of social phenomena rather than mere descriptions. While the program addresses topics of societal significance and policy relevance, the nature of the program is knowledge driven: the program explicitly aims at solving sociological and social science problems while contributing to the advancement and growth of theoretical and empirical knowledge in sociology and social science.

The research strategy emphasizes a problem-driven approach. Deductive theory building and the integration of theory formation and empirical research are core elements of the research strategy. Thus, hypotheses to be tested are systematically derived from theories with an aim to develop general theories that allow for the integration of more specific theories by correcting and improving them. The research strategy thus contributes to the accumulation of coherent social science knowledger and the reduction of the fragmentation of social science. An interdisciplinary orientation that avoids excluding theories or methods on the basis of their disciplinary origin is an important ingredient of this research strategy. The research strategy is applied in a broad range of research domains and to specific topics and research problems (see below, section on research groups). This ensures ‘unity in diversity’: On the one hand, the common research strategy and common field of interest provide for coherence and continuity of the program and ensure opportunities for and attractiveness of communication and collaboration between program members. On the other hand, the different research lines induce pluralism and opportunities for mutual stimulation, criticism, and the development of fresh ideas. The different research lines thus allow for flexibility and vitality by ensuring the evolving nature of the program. They likewise ensure diversification of risks for the program as a whole in the sense that quality and continuity of the program do not depend on one single approach.

Structural individualism is a major starting point for theory building. This means that social phenomena are explained as a result of purposive behavior of individuals as well as corporate social actors. Typically, social phenomena are explained as a result of interdependent action and often also as an unintended result of purposive behavior. Purposive behavior is shaped by the constraints and opportunities posed by the social and institutional context, including ties and interdependencies between actors. The focus of theory building is not on the explanation of individual action as such, but on how the social and institutional context conditions behavior and how the context is transformed through the effects of actors’ behavior: a so-called ‘social mechanism’ view of explanation, including the macro-micro-macro question.

Appropriate data sets are required for the empirical testing of theories and hypotheses. Therefore the ICS dedicates much time and effort to the collection of high quality data. Structural individualism requires that data sets comprise individual as well as contextual information, hence multi-actor, multi-level, and multi-event data sets are needed. These data sets should include: (a) information about the actors and their interdependence with respect to constraints, actions, and outcomes, (b) information about the various levels of aggregation (individual, various types of context), and (c) information about multiple events over time that can provide information on how later events are conditioned by earlier ones. Different and complementary kinds of data are employed, including not only large-scale survey data, but also, for example, historical data and archival data, experiments, and data from case studies. Employing such complex data sets for testing hypotheses requires, in turn, appropriate statistical models that express mathematically the main theoretical relations between the observed variables bearing on the various actors, levels, and events.