Suchergebnisse

In crisis management, scenario planning is a necessity in our volatile, uncertain, complex, and ambiguous world. Strategic planners need to be able to imagine future environments even in the most uncertain conditions. The scale of potential scenarios and associated management suggests that those involved will require innovative planning techniques. To support innovation in strategic planning, we have combined the research on creativity, divergent thinking, and creative constraints to design a method called "Stretch‐Thinking Loops." This technique uses iterative thinking approaches that identify broad scenarios, the likely consequences, and the potential constraints, and then uses this information to identify new opportunities and innovations to support scenario planning. We report on the development of this technique in the context of crisis management and its application in a collaborative project with an Australian State Government that explored a series of post‐COVID‐19 recovery scenarios for a 12‐month time horizon. The proposed Stretch‐Thinking Loops are not just limited to crisis management but offer all organizations a structured method to enhance their capability to engage in divergent‐thinking for future scenario planning.

A signal is a description of an information source that specifies both its correlation with the state and its correlation with other signals. Extending Blackwell (1953), we characterize when one signal is more valuable than another regardless of preferences and regardless of access to other signals. This comparison is equivalent to reveal-or-refine: every realization of the more valuable signal reveals the state or refines the realization of the less valuable signal. We also study other comparisons of signals, including sufficiency, martingale dominance, and Lehmann. Reveal-or-refine is also equivalent to making any of these comparisons robust to access to other signals. (JEL C90, D82, D83)

The accepted detrimental effects of climate change and the anticipated increased frequency of cascading disasters means there is a pressing requirement to equip search and rescue teams with the capability to perform effective and complex risk assessments. This paper investigates risk-based decision-making expertise in the aftermath of the 2011 earthquake and tsunami in Japan. It compares the actual decisions made by an Urban Search and Rescue (USAR) commander, with the decisions that a cohort of people working within search and rescue made, when provided with the same decision context using 3 vignettes. Variations in the results are explored in terms of the complexity of the risk decision and the type of expertise required. The findings indicate that as the risk becomes more complex, the percentage of answers that were the same as the USAR commander (that we deem as 'correct' as they did not result in any adverse outcomes for the USAR team) decreased. Training entities need to provide decision-makers with the necessary human capabilities so they can perform the complex risk assessments required to make decisions in low-probability yet high-consequence disasters.

We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoff-relevant states of the world are unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about the state. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents' information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional. (JEL D44, D82, D83)

AbstractThis paper provides an insight into the complexities of decision‐making during an unprecedented disaster. We used the critical decision method to explore a series of decision points that were made for a low probability yet high consequence decision that was made by the commander of the Australian Urban Search and Rescue team deployed to Fukushima in 2011. The findings identified that in a situation with no similarities to previous experiences, the commander used a process of anomaly detection to trigger a situational assessment, following this with mental simulation and consultation of his actions. In this unparalleled case study, hazard‐specific expertise also supported the decision‐making process. The paper offers practitioners and academia an example of high consequence decision‐making in a unique situation as well as the opportunity to reflect on the models of decision‐making previously identified as useful in these operational environments.