Tackling Presentist Bias: Conceptualizing Long-Term Governance

It is well documented that democratic systems suffer from a presentist bias and structural discounting of future risks, evident in issues like climate change, pension system and governance of emerging technologies. However, the solution space, loosely termed ‘long-term governance’, remains conceptually fragmented. Scholars employ a plethora of terms, often conflating normative goals (like intergenerational justice) with procedural remedies (like anticipatory governance or foresight). This paper contributes to the anticipatory democracy research agenda by systematically mapping this landscape, answering the call for a unified framework concept that delineates the necessary and sufficient conditions for governing the long-term (see Scheer et al., 2025). The paper proposes a holistic theoretical model that distinguishes three logical dimensions of the field: 1. Normative level: This dimension categorizes the justifications for long-term governance. For example, intergenerational justice can be approached either from a distributive model (allocating resources) or a relational model clarifying how concepts like presentism function as normative critiques of temporal power imbalances (Campos, 2024). 2. Descriptive level: Clarifies the definition of the problem. By conceptualizing political short-termism or myopia as a conditional phenomenon dependent on specific drivers (e.g., electoral cycles, volatility), the framework allows for a more precise diagnosis of where democratic temporal alignment fails (Ogami, 2024; Koskimaa & Rapeli, 2025). 3. Governance level: This aspect maps the mechanisms of governance. It organizes the solution space along a spectrum of policy interventions, from analytical capacity (strategic foresight) to institutional design (insulation and commitment devices), thereby clarifying the trade-offs between binding and flexible instruments (Boston, 2017; MacKenzie et al., 2023). Finally, the paper demonstrates the analytic utility of this framework in the context of risks of emerging technologies. By applying these conceptual distinctions to emerging technologies like AI, the analysis reveals how the framework captures temporal dynamics that traditional divisions miss. Specifically, it highlights how technological path dependencies require a distinct mode of future-proof intervention; one that emphasizes anticipatory action over post-hoc liability. This comparison validates the framework’s capacity to differentiate between policy problems, showing that long-term governance is not a monolithic concept but sensitive to institutional contexts (Pot et al., 2020; Leruth, 2024 ). References: Boston, J. (2017). Governing for the future: Designing democratic institutions for a better tomorrow. Bingley: Emerald. Campos, A. S. (2024). The Semi-Future Democracy: Governing for the Future in Liberal Democracies. Edinburgh University Press. Leruth, B. (2024). Political long‐termism and the European Union: Five research questions for the future. Contemporary European Politics, 2(2), e70000. Koskimaa, V., & Rapeli, L. (2025). Advancing Future-Orientation in Policymaking: Institutions, Individuals and Risks (p. 180). Taylor & Francis. Ogami, M. (2024). The conditionality of political short‐termism: A review of empirical and experimental studies. Politics and Governance, 12. Pot, W., Scherpenisse, J., & 't Hart, P. (2023). Robust governance for the long term and the heat of the moment: Temporal strategies for coping with dual crises. Public Administration, 101(1), 221-235. Scheer, D., et al. (2025). No easy way out: towards a framework concept of long-term governance. Energy, Sustainability and Society, 15(1), 9.