policy

Modelling collective decision-making in policy networks

Modelling collective decision-making in policy networks

Public policy-making - the set of intentions, decisions, and processes that lead to the creation, implementation and recycling of policies within governments and international organizations - takes place in a complex system (Geyer and Cairney 2015). This system is characterised by heterogeneous actors pursuing their idiosyncratic goals in an institutional context and a changing environment that rhythms the course of and affects the content of policy decisions (Geyer and Rihani 2010, Morçöl 2013, Koliba et al. 2018). Zooming in on the micro-level, one can observe policy actors’ cognitive complexity, usually conceptualised as ‘bounded- rationality’ (Simon and March 1976; Dente 2014; Hertwig and Pedersen 2015; Zahariadis and Herweg 2017). In the aggregate, policy dynamics satisfy the hallmarks of complex systems:

Tactical models to improve institutional decision-making

Tactical models to improve institutional decision-making

This post presents reflections on how to improve the work of governments and international organisations. It focuses in particular on the role of institutional decision-making, as this seems to be a concrete and feasible avenue of fostering policy-makers’ impact. This post does not try to explain why one should (not) work on improving policy-making.

First of all, we propose that approaching policy-making systematically can be roughly done as follows:

  1. Understand policy-making dynamics

  2. Define tactics to approach policy-making

  3. Implement techniques (e.g. calibration training) [or hyperlink to resource on website]

  4. Evaluate impact and feed learnings back to 1-2-3

Jess Whittlestone’s post on improving institutional decision-making provides useful high-level approaches:

  • test and evaluate existing techniques

  • research alternatives techniques

  • fostering adoption of techniques

  • direct more funding to the above

… which fall under 3. and 4.

Our post complements Whittlestone’s by presenting three models that inform 2. and thus help calibrating an outside actor’s approach to improving institutional decision-making. These models come from the literature review we conducted for forthcoming publications which attempt to cover point 1. of understanding policy-making dynamics.

An agent-based model of policy-making: disaggregation and conceptualization

An agent-based model of policy-making: disaggregation and conceptualization

Policy-making exhibits nonlinear patterns which emerge from the interaction of heterogeneous bounded-rational actors. How to understand such complexity with both clarity and parsimony? We disaggregate policy-making into different parts to develop a conceptual agent-based model. We define agents with attributes, cognitive rules, and strategies drawing from the literature on public policy, sociology, and behavioral psychology. We present a set of parameters that constrain agent adaptation, such as environmental variables; policy processes; and network structures. We use bit-strings to encode information and the fundamentals of evolutionary biology to conceptualize learning and adaptation mechanisms. We present a set of hypothetical results for illustration purposes. This disaggregation and conceptualization serves as the groundwork to formalize, implement, and simulate the current understanding of policy-making. Among other applications, the model is designed to inject dynamism into static theory; shed light on how individual behavior self-organizes and scales to the group and the institutional levels; and conduct evaluative research in data-scarce contexts.

An Introduction to Complexity Science for Social Sciences

An Introduction to Complexity Science for Social Sciences

In both social sciences and policy-making, researchers and practitioners tackle multifaceted phenomena. Examples are (armed) conflicts, migration, the emergence of populism, the automation of professions, financial crises, international trade, or social integration. Efforts in research and practice have led to various approaches and processes to analyse these phenomena and make decisions under uncertainty.

I argue that the toolbox used to tackle these real-world problems could benefit a lot from the growing field of complexity science, i.e. the study of complex systems in the physical, biological, and social worlds. I believe complexity science is a vital tool that yields a more honest and granular understanding of social phenomena. Complexity science is not revolutionary, it is the middle-ground between the assemblage of (a) insights and methods from many scientific disciplines and (b) the dilution of disciplinary boundaries.