AMP Toolbox

Forward looking analysis: Assess policy success – and risk factors
Introduction

Foresight analysis offers policy makers a way to view policy design retrospectively, prospectively and comprehensively. Policies can be made robust to a range of anticipated conditions, (i) identifying key factors that affect policy performance, (ii) identifying scenarios to study the way these factors might evolve in the future, and (iii) using developed indicators to help trigger important policy adjustments when needed. These types of analyses are embodied in an approach referred to as scenario planning. In general, scenarios contain a description of step-wise changes or a storyline, driving forces, base year, and time steps and horizon. Foresight analysis for adaptive policy making requires developing quantitative exploratory scenarios relying on modelling tools incorporating quantified information to calculate future developments and changes.
According to Williams et al. (2012) models play a key role in representing uncertainty. In adaptive management, structural or process uncertainty is captured in contrasting hypotheses about system structure and function, and the hypotheses are imbedded in the suite of models used to forecast resource changes through time. In order to make smart decisions, it is always important to compare and contrast management alternatives in terms of their costs, benefits, and environmental consequences. Models typically express benefits and costs as outputs of management through time. More importantly, they allow forecasting of the impacts of measures. The term “model” is used here to mean a plausible representation of a dynamic environmental system. Models can be as informal as a verbal description of system dynamics, or as formal as a detailed mathematical expression of change, or also an integrated model, such as those developed by the PERSEUS Project.

Key questions
  • What are the key factors that may affect policy performance?
  • What scenario story lines embody the key factors?
  • How to quantify these scenarios using predictive models?
  • What policy measures will perform well in the anticipated future conditions?

Key actions

The following key actions are detailed in Swanson and Tomar (2009).

Identifying key factors that affect policy performance
  • This action is better accomplished in a deliberative process with multiple stakeholders, experts involved in implementation of the policy and those who are impacted (positively or negatively) by the policy.

Developing scenarios for the plausible evolution of these key factors
  • Potential future evolution of the key factors can be projected using a combination of qualitative and quantitative methods.
  • Scenarios are a coherent package of key factors. Coherence is achieved by understanding the higher-level drivers for these key factors and how these drivers influence the various key factors.
  • Scenarios are quantified using predictive models. See Williams et al. (2009) on recommendations about the model to be used to draft adaptive policies in environmental resources management.

Addressing key adaptive policy design questions
  • Can a policy option be developed to perform in a range of anticipated future conditions?
  • What are the potential adverse and unintended impacts of the policy and what actions can be taken now to mitigate and hedge against the consequences?
  • How might the policy need to be adjusted in the future in order to continue performing successfully and how will the adjustment be triggered?

But not all situations can be anticipated. Unknown unknowns and deep uncertainty will always be part of policy making. Adaptive policies are able to navigate toward successful outcomes in settings that cannot be anticipated in advance. This can be done by working in concert with certain characteristics of complex adaptive systems and thereby facilitating autonomous actions among stakeholders on the ground. These actions include: (i) enabling self-organisation and social networking; (ii) decentralising decision making to the lowest and most effective jurisdictional level; and (iii) promoting variation in policy responses. These tools are presented in Swanson and Tomar (2009) and Swanson et al. (2010).

Tools & methods
  • DPSWR framework
  • Habitat Priority Planner
  • InVEST Toolbox
  • LINK
  • MarineMap
  • MARXAN
  • PANDA
  • SimLab
  • PERSEUS presentation materials
  • Social and economic assessment methods
  • Quantitative stock assessment methods
  • Asset/objective – impact/threat matrix
  • Conceptual and qualitative modelling

Resources dedicated to the Mediterranean and Black Sea marine environment


Further reading