The futility of decision making research

We have each spent more than 50 years doing research that has had little impact. Even more lamentable is that our field, judgment and decision making (JDM), has on the whole had little impact during that span. We attribute that failure to the use of methodologies that emphasize testing models rather than looking for differences in behavior. The “cognitive revolution” led the field astray, toward the goal of studying model fit rather than comparing observable results. With modeling as the goal, experimentation was stultified. Simple tasks became dominant. Although a poor metaphor for real decision making, the gambling paradigm has lasted forever because the inputs to the decision are known to the researcher and thus easily modeled.     

Criterion-referenced judgemental forecasting models

Human judgements are usually made with reference to a criteria such as cost or profit. When making a judgement model for such a decision, the ideal is for the model to yield near-optimal results in terms of the criteria. This article starts with the theory behind concave criteria and then develops a mathematical argument which gives guidance to judgemental model building. This guidance departs from the conventional wisdom of using only R-squared in model selection.     

Prediction,diagnosis, and causal thinking in forecasting

While forecasting involves forward/predictive thinking, it depends crucially on prior diagnosis for suggesting a model of the phenomenon, for defining‘relevant’variables, and for evaluating forecast accuracy via the model. The nature of diagnostic thinking is examined with respect to these activities. We first consider the difficulties of evaluating forecast accuracy without a causal model of what generates outcomes. We then discuss the development of models by considering how attention is directed to variables via analogy and metaphor as well as by what is unusual or abnormal. The causal relevance of variables is then assessed by reference to probabilistic signs called‘cues to causality’. These are: temporal order, constant conjunction, contiguity in time and space, number of alternative explanations, similarity, predictive validity, and robustness. The probabilistic nature of the cues is emphasized by discussing the concept of spurious correlation and how causation does not necessarily imply correlation. Implications for improving forecasting are considered with respect to the above issues.     

Climate change transformation: A definition and typology to guide decision making in urban environments

Climate change presents a threat to the sustainability of cities and their societies, and must be adequately addressed. Urban environments (cities) are responsible for the creation of a significant amount of greenhouse gas emissions which are the source of climate change. Cities have been increasingly the focus of action to address climate change, yet emissions are not significantly reducing. Additionally, there a lack of integration between adaptation and mitigation. This prevents responses adequate to limit global warming to 1.5^OC, and to be well adapted to anticipated changes. This paper critically analyses existing definitions and typologies of climate change actions. A definition of ‘climate change transformation’ is proposed which includes the integration of adaptation and mitigation goals to enable a new regime in which global warming is limited to 1.5^OC. A new three-part typology: ‘coping, malaction and transformation,’ is presented for categorising climate change actions by the extent to which they integrate adaptation and mitigation, and define a new regime. The typology is accompanied by illustrations to demonstrate the relationship between adaptation and mitigation. The definition, typology and illustration serve to guide effective climate change decision making, and provides principles to guide application in urban environments.     

Climate change transformation: A definition and typology to guide decision making in urban environments

Climate change presents a threat to the sustainability of cities and their societies, and must be adequately addressed. Urban environments (cities) are responsible for the creation of a significant amount of greenhouse gas emissions which are the source of climate change. Cities have been increasingly the focus of action to address climate change, yet emissions are not significantly reducing. Additionally, there a lack of integration between adaptation and mitigation. This prevents responses adequate to limit global warming to 1.5^OC, and to be well adapted to anticipated changes. This paper critically analyses existing definitions and typologies of climate change actions. A definition of ‘climate change transformation’ is proposed which includes the integration of adaptation and mitigation goals to enable a new regime in which global warming is limited to 1.5^OC. A new three-part typology: ‘coping, malaction and transformation,’ is presented for categorising climate change actions by the extent to which they integrate adaptation and mitigation, and define a new regime. The typology is accompanied by illustrations to demonstrate the relationship between adaptation and mitigation. The definition, typology and illustration serve to guide effective climate change decision making, and provides principles to guide application in urban environments.     

Climate change transformation: A definition and typology to guide decision making in urban environments

Climate change presents a threat to the sustainability of cities and their societies, and must be adequately addressed. Urban environments (cities) are responsible for the creation of a significant amount of greenhouse gas emissions which are the source of climate change. Cities have been increasingly the focus of action to address climate change, yet emissions are not significantly reducing. Additionally, there a lack of integration between adaptation and mitigation. This prevents responses adequate to limit global warming to 1.5^OC, and to be well adapted to anticipated changes. This paper critically analyses existing definitions and typologies of climate change actions. A definition of ‘climate change transformation’ is proposed which includes the integration of adaptation and mitigation goals to enable a new regime in which global warming is limited to 1.5^OC. A new three-part typology: ‘coping, malaction and transformation,’ is presented for categorising climate change actions by the extent to which they integrate adaptation and mitigation, and define a new regime. The typology is accompanied by illustrations to demonstrate the relationship between adaptation and mitigation. The definition, typology and illustration serve to guide effective climate change decision making, and provides principles to guide application in urban environments.     

Beyond heuristics and biases: A contingency model of judgemental forecasting

The conflicting viewpoints about the quality of judgemental forecasts are examined and a model is proposed that attempts to resolve the conflict. The model sees forecasts as contingent upon the repertory of forecasting strategies that the forecaster brings to the forecasting task, the strategy that he or she selects as a function of the characteristics of the task, and the rigour with which he or she applies the strategy as a function of the motivating characteristics of the environment in which the task is encountered. The implications of differences in subjects' and experimenters' assumptions about which strategies are appropriate in experimental studies are examined, as are the implications of the differences between the motivating aspects of experimental and applied settings on both performance and on the generatizability of the results of experiments to applied judgemental forecasting.     

Evaluating forecasts: a look at aggregate bias and accuracy measures

In this paper an investigation is made of the properties and use of two aggregate measures of forecast bias and accuracy. These are metrics used in business to calculate aggregate forecasting performance for a family (group) of products. We find that the aggregate measures are not particularly informative if some of the one‐step‐ahead forecasts are biased. This is likely to be the case in practice if frequently employed forecasting methods are used to generate a large number of individual forecasts. In the paper, examples are constructed to illustrate some potential problems in the use of the metrics. We propose a simple graphical display of forecast bias and accuracy to supplement the information yielded by the accuracy measures. This support includes relevant boxplots of measures of individual forecasting success. This tool is simple but helpful as the graphic display has the potential to indicate forecast deterioration that can be masked by one or both of the aggregate metrics. The procedures are illustrated with data representing sales of food items. Copyright © 2005 John Wiley & Sons, Ltd.     

When and how to use scenario planning: A heuristic approach with illustration

This paper offers a step-by-step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key-success-factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.     

Model uncertainty and policy choice: A plea for integrated subjectivism

A question at the intersection of scientific modeling and public choice is how to deal with uncertainty about model predictions. This “high-level” uncertainty is necessarily value-laden, and thus must be treated as irreducibly subjective. Nevertheless, formal methods of uncertainty analysis should still be employed for the purpose of clarifying policy debates. I argue that such debates are best informed by models which integrate objective features (which model the world) with subjective ones (modeling the policy-maker). This integrated subjectivism is illustrated with a case study from the literature on monetary policy. The paper concludes with some morals for the use of models in determining climate policy.     

Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co‐integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out‐of‐sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model. Copyright © 2015 John Wiley & Sons, Ltd.