Severe uncertainty and info-gap decision theory

Managing uncertainty in decision-making for conservation science

Science-based decision-making is the ideal. However, scientific knowledge is incomplete, and sometimes wrong. Responsible science-based policy, planning, and action must exploit knowledge while managing uncertainty. I considered the info-gap method to manage deep uncertainty surrounding knowledge that is used for decision-making in conservation. A central concept is satisficing, which means satisfying a critical requirement. Alternative decisions are prioritized based on their robustness to uncertainty, and critical outcome requirements are satisficed. Robustness is optimized; outcome is satisficed. This is called robust satisficing. A decision with a suboptimal outcome may be preferred over a decision with a putatively optimal outcome if the former can more robustly achieve an acceptable outcome. Many biodiversity conservation applications employ info-gap theory, under which parameter uncertainty but not uncertainty in functional relations is considered. I considered info-gap models of functional uncertainty, widely used outside of conservation science, as applied to conservation of a generic endangered species by translocation to a new region. I focused on 2 uncertainties: the future temperature is uncertain due to climate change, and the shape of the reproductive output function is uncertain due to translocation to an unfamiliar region. The value of new information is demonstrated based on the robustness function, and the info-gap opportuneness function demonstrates the potential for better-than-anticipated outcomes.

Earthquake Vulnerability Reduction by Building a Robust Social-Emotional Preparedness Program

Despite the progress made in understanding the characteristics of earthquakes, the predictions of earthquake activity are still inevitably very uncertain, mainly because of the highly complex nature of the earthquake process. The population′s mental strength is of high importance not only to cope with an earthquake, but also to return quickly to functioning. Social-emotional preparedness for extreme adverse events and crises is a critical factor in the population’s quick recovery and return to full functioning. In the present study, we apply a multi-disciplinary lens to extend the scope of earthquake preparedness to include social-emotional programs. The goal of this study is to develop a robust “no-regret” social-emotional preparedness program (SEPP) along with methodological tools for evaluating the SEPP robustness against uncertainty in different earthquake scenarios. The research methodology is twofold. First, we develop the SEPP based on social-emotional proxies-for-robustness, and second, we apply the info-gap decision theory (IGDT) methods to assess the robustness of the SEPP in the face of uncertainty in different earthquake scenarios. The findings indicate gaps between the level of robustness of the SEPP in different scenarios. A key conclusion that emerges from this study is the need for a robust SEPP to make a significant contribution to the population’s ability to return to functioning. Such SEPP should be formulated to maximize the robustness against uncertainty in different scenarios, rather than the traditional planning based on a ‘one size fits all’ approach. Formulating a robust SEPP by analyzing the robustness of the SEPP against uncertainty will enable to make decisions immune to surprises. Ways to create or improve earthquake preparedness are suggested for policy and in-school application.

Info-Gap Models for Optimal Multi-Year Management of Regional Water Resources Systems under Uncertainty

The common practices for the planning and management of Water Resources Systems (WSSs) have been challenged in the last few decades by global climate change processes, which are observed around the world in increasing frequencies. Climate change is manifested by climate variability, temperature increase, and extreme events such as droughts and floods, which have a decisive effect on natural resource availability and in turn on water quality. Historical records may not be sufficient to reliably account for uncertain future predictions under climate change conditions. While such highly uncertain situations become the “normal” case worldwide, the traditional practices of probabilistic risk measures cannot be used to appropriately quantify the uncertain phenomena under non-stationarity conditions. To better account for uncertain future conditions, the objective of this study is to develop a water management model based on Info-Gap Decision Theory (IGDT) using optimization under deep uncertainty conditions. The Info-Gap theory is a framework that measures the confidence in the operational decisions by quantifying the robustness to uncertainty without accounting for any probabilistic data. To demonstrate the method as a tool to better guide the long-term sustainable operation of the water supply system under uncertain future conditions, we applied the Info-Gap model to the Sea of Galilee (SoG) regional WSS, which is a significant part of the Israeli National Water System (INWS). For Israel, which is, like other Middle East semi-arid regions, prone to dry conditions and limited water availability, there are well-founded concerns that prolonged periods of drought lie ahead, as a consequence of the global climate change processes. This study contributes a management tool for decision making under deep uncertainty to improve the decision-making process and better adapt to unpredictable uncertain future conditions. We demonstrate how the IGDT could be formulated and used to analyze WSSs under different settings and demonstrate how decisions could be derived from the IGDT formulation. We also show a sensitivity analysis for the obtained solutions.

A multidisciplinary conceptualization of conservation opportunity

An opportunity represents an advantageous combination of circumstances that allows goals to be achieved. We reviewed the nature of opportunity and how it manifests in different subsystems (e.g., biophysical, social, political, economic) as conceptualized in other bodies of literature, including behavior, adoption, entrepreneur, public policy, and resilience literature. We then developed a multidisciplinary conceptualization of conservation opportunity. We identified 3 types of conservation opportunity: potential, actors remove barriers to problem solving by identifying the capabilities within the system that can be manipulated to create support for conservation action; traction, actors identify windows of opportunity that arise from exogenous shocks, events, or changes that remove barriers to solving problems; and existing, everything is in place for conservation action (i.e., no barriers exist) and an actor takes advantage of the existing circumstances to solve problems. Different leverage points characterize each type of opportunity. Thus, unique stages of opportunity identification or creation and exploitation exist: characterizing the system and defining problems; identifying potential solutions; assessing the feasibility of solutions; identifying or creating opportunities; and taking advantage of opportunities. These stages can be undertaken independently or as part of a situational analysis and typically comprise the first stage, but they can also be conducted iteratively throughout a conservation planning process. Four types of entrepreneur can be identified (business, policy, social, and conservation), each possessing attributes that enable them to identify or create opportunities and take advantage of them. We examined how different types of conservation opportunity manifest in a social-ecological system (the Great Barrier Reef) and how they can be taken advantage of. Our multidisciplinary conceptualization of conservation opportunity strengthens and legitimizes the concept.

Contending with uncertainty in conservation management decisions

Efficient conservation management is particularly important because current spending is estimated to be insufficient to conserve the world's biodiversity. However, efficient management is confounded by uncertainty that pervades conservation management decisions. Uncertainties exist in objectives, dynamics of systems, the set of management options available, the influence of these management options, and the constraints on these options. Probabilistic and nonprobabilistic quantitative methods can help contend with these uncertainties. The vast majority of these account for known epistemic uncertainties, with methods optimizing the expected performance or finding solutions that achieve minimum performance requirements. Ignorance and indeterminacy continue to confound environmental management problems. While quantitative methods to account for uncertainty must aid decisions if the underlying models are sufficient approximations of reality, whether such models are sufficiently accurate has not yet been examined.

Response to Burgman and Regan: the elephant in the rhetoric on info-gap decision theory

The formal, rigorous assessment of IGDT in Sniedovich (2012) reveals that this theory's central pillar, namely its robustness model, is a reinvention of a well-established model of local robustness, known universally as radius of stability (circa 1960). As a matter of fact, this robustness model is a simple model derived from Wald's famous maximin paradigm (circa 1940). This means that had there been any gap in the state of the art that IGDT could have possibly presumed to fill, this gap had already been filled decades ago, well before IGDT was even contemplated. The conclusion therefore is that there is no gap in the state of the art that IGDT does fill, or can possibly fill, or is called upon to fill. Also, since IGDT is based on a definition of local robustness, the theory is unsuitable for the treatment of a severe uncertainty of the type that this theory claims to address. Therefore, since the theory claims to be particularly suitable for the treatment of a severe, unbounded uncertainty, the inevitable conclusion is that this theory constitutes a voodoo decision theory par excellence. Fig. 1 speaks for itself so that no amount of rhetoric can explain this fact away. The Letter's attempt to brush off valid, rigorous, well-documented criticism of IGDT as "... haggling over terminology ..." is yet another attempt to avoid dealing with the elephant in the IGDT room. Nothing will be gained from the use of misleading rhetorics to argue that ideas, models, techniques, approaches, etc., that go back to the 1940s and 1960s, are IGDT innovations. But more than this, what good can come of misapplications of these ideas in applied ecology and conservation biology? In the Appendix, I address a more intriguing question, namely: QUESTION 2: What could possibly be the rationale that motivated a search for a (nonexistent) gap in the state of the art for IGDT to fill?

The potential for meta-analysis to support decision analysis in ecology

Meta-analysis and decision analysis are underpinned by well-developed methods that are commonly applied to a variety of problems and disciplines. While these two fields have been closely linked in some disciplines such as medicine, comparatively little attention has been paid to the potential benefits of linking them in ecology, despite reasonable expectations that benefits would be derived from doing so. Meta-analysis combines information from multiple studies to provide more accurate parameter estimates and to reduce the uncertainty surrounding them. Decision analysis involves selecting among alternative choices using statistical information that helps to shed light on the uncertainties involved. By linking meta-analysis to decision analysis, improved decisions can be made, with quantification of the costs and benefits of alternate decisions supported by a greater density of information. Here, we briefly review concepts of both meta-analysis and decision analysis, illustrating the natural linkage between them and the benefits from explicitly linking one to the other. We discuss some examples in which this linkage has been exploited in the medical arena and how improvements in precision and reduction of structural uncertainty inherent in a meta-analysis can provide substantive improvements to decision analysis outcomes by reducing uncertainty in expected loss and maximising information from across studies. We then argue that these significant benefits could be translated to ecology, in particular to the problem of making optimal ecological decisions in the face of uncertainty.

Supporting fisheries management by means of complex models: can we point out isles of robustness in a sea of uncertainty?

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.