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An overview of Week 2

In Week 1 you were introduced to the policy dilemma – which policy will give the desired outcomes and not have undesirable consequences – in the context of various examples of global systems.

This week begins by considering prediction. In general social systems are not predictable in the conventional sense of the traditional sciences.

One of the reasons for this is the complexity and messiness of social systems. Generally any social system has many interacting subsystems with many stakeholders having competing interests. Science can help here by helping to make clearer and allowing us to analyse social systems and their possible behaviours.

In Global Systems Science the focus is on science providing useful methods and tools for policy makers. Generally those tools are computer programs and procedures for processing data to investigate the outcomes of policy and present the result in ways that everyone can understand. We call this ‘informatics for policy’ or policy informatics.

Policy does not pop up from nowhere. It emerges from processes of reflection that identify new or changing needs and constraints, generate and evaluate possible ways of satisfying them, and decide between the various options. In some sense, policy is designing the future and this perspective illuminates the place of science in the policy process. Science helps policy makers to generate and evaluate feasible alternatives. Deciding between alternatives is not the prerogative of science – this is the prerogative of politicians and citizens.

By its nature, policy emerges from evolving narratives of how the world is, how the world ought to be, and how to make it as it ought to be. These narratives reflect different beliefs and values and their evolutionary dynamics can play a significant role in policy selection and implementation. Although in its infancy, this new science is emerging that may help us better understand the narratives of policy.

Without doubt Global Systems Science can make a big contribution to policy makers, but it has its limitations. The course will end with a discussion of what Global Systems Science can contribute now, what it cannot contribute now, and what it might contribute in the future.

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This article is from the free online course:

Global Systems Science and Policy: an Introduction

UNESCO UNITWIN Complex Systems Digital Campus

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join:

  • Policy Makers including presidents, directors of NGOs, and citizens
    Policy makers
    article

    In Global Systems Science this article defines policy makers are defined to be politicians, their officers, citizens and other stakeholders.

  • Prediction and the policy dilemma
    Prediction and the policy dilemma
    video

    The Policy Dilemma involves policy makers trying to predict if their policies will work. This article explains why prediction is so hard.

  • Policy design
    Policy design
    video

    In this video Jeffrey Johnson explains that policy, like design, is a coevolution between problem and solution involving compromise and satisficing.

  • Conclusion to the course
    Conclusion to the course
    article

    This article concludes the Global System Science and Policy course by noting that Complex Systems Science is young and cannot solve all problems.

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