| Abstract [eng] |
The thesis explores the contrast between data-driven policy-making and the heuristics of political elites as decision-making individuals, empirically examining the case of higher education policy, through decisions taken by the Ministers of Education, Science, and Sport on the preliminary allocation of state-funded undergraduate study places by field of study between 2016 and 2023 in Lithuania. The thesis aims to investigate the foundations of decision-making through the decision of allocating the study places: is it data-driven or based on cognitive bias? The data-driven decision-making perspective in this thesis is linked to the rational decision making approach, primarily based on the principles of analytical research. It is expected for the decision-making process to start with formulation and determination of the objectives, keeping the same objectives within the analysis synchronizing to the decision-making process, identifying alternatives, evaluating the consequences, and selecting the alternative of the best chance of achieving the desired objective. On the contrary, in the case of the political elites, it is argued that the amount of information available for each decision is enormous, it is difficult to assess and make good use of it, and the uncertainty conditions with multiple factors surround and complicate the decision-making process. That creates conditions for cognitive biases to emerge as the mechanisms or systems of cognitive shortcuts, with limitations of rational thinking. This thesis explores political elite decisions within three selected heuristics: the anchoring effect, availability, and representativeness heuristics. In this thesis 3 hypotheses were formulated, two of which based on different interpretations of the theoretical approach: H1: Lithuanian higher education policy decisions on the allocation of state funded undergraduate study places by field of study in 2016-2023 were based on data analysis and H2: decisions were based on the previous year’s distribution decision or rely on heuristics of accessibility or representativity. The third hypothesis examines the basis for a decision taken by the same decision-maker, depending on the recurrence of the decision: H3: Decision-making processes differ depending on whether the decision needs to be made for the first time, or is recurrent. The study finds that decision-making in higher education policy, for allocation of state-funded undergraduate places, is geared towards 'rational/data-driven' decision-making. Therefore, mechanisms of the process are characterized by a certain level of inertia, as the actors in the subsystem are closely interlinked. Despite the complexity of the system, the preconditions for decision-making exist, and public policy decisions can be made both based on data and influenced by cognitive heuristics. The first hypothesis was supported partially by the analysis, which identified cases where data were used in decision-making, aiming for the most accurate analytics, and reconsidering decisions relatively often in critical cases with new information occurrence. The hypothesis, focusing on cognitive heuristics, was fully supported: empirical implications of all three biases observed in individual cases. The third hypothesis was rejected by empirical research. None of the Ministers showed significant differences in the basis of making decisions between the first and subsequent periods. This thesis is in a heretofore under-researched field – contrasting the doctrines of data-driven decision-making and biases in the decisions of political elites in the higher education system. It contributes to both theoretical and practical discussions on political elites' decision-making, not only demonstrating this particular decision basis but also revealing the mismatch of the design of the policymaking process for data-driven decisions and empirical cases that show preconditions for decisions to appear on cognitive biases. |
