Robust decision making and scenario discovery in the absence of formal models

Abstract

Robust decision making (RDM) is a method for aiding decision making under deep uncertainty that uses models not as predictors but as generators of cases exploring assumptions and outcomes. RDM was intended for use with formal models. However, we show a model‐less RDM application to a portfolio planning problem (selecting U.S. Army security cooperation activities with a partner country) seeking to achieve several objectives. In the absence of formal models, the analysis tests candidate actions against different explicit statements of causal relationships to allow more systematic reasoning over choices and outcomes. Doing so renders assumptions about complex systems explicit, characterizes uncertainties in terms of effect on weighting policy choices rather than as presently unknowable probabilities, provides a venue for planners and evaluators to share findings and insights, and yields explicit expressions of theories of causation (TOC) that themselves act as formal models where none had previously existed. RDM allows important scenarios for decision planning to be generated analytically. This may address for several scenario applications the question of determining important futures and also how scenario results may be used directly to inform policy and operational decisions.

Free will determines the limits of psychological foresight: Review of “Free Will” by Sam Harris

“…By believing in free will as an indeterminable and irreducible part in the equation of worldly causes we have to accept that psychological foresight is not truly possible anymore. As shown in the review, this consequence is relevant for an abundance of scientific and applied fields concerned with human psychology, also central to the F&FS community -like organizational management (e.g., Derbyshire, 2021;Schoemaker, 2018;Karjalainen & Heineken, 2018), historic and strategic analysis (e.g., Schoemaker;Tetlock, 2020) and explicit prediction models (e.g., Popper, 2019;Fergnani & Chermack, 2020). Believing in free will is up to each individual's own discretion.…”

“…The belief in free will, cF, reduces these two sources' assumed impact. It decreases the predictive power of any theory about human cognition and behavior, necessitating other forms of modelling (e.g., Popper, 2019).…”

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

“…Using these methods still leads to increased computational needs compared to the simpler robustness criteria, and data requirements can be significant. Future work could expand on the possibility to analyze DMDU for green infrastructure without formal models, for example, with increased use of expert analyses or stakeholder expertise (Popper, 2019). Expert elicitation is an increasingly popular way to assess the deep uncertainty associated with climate change decision making, for example, for precipitation patterns (Dessai et al., 2018), energy (Usher & Strachan, 2013), and sea level rise (Garner et al., 2018).…”

Stay competitive in 2035: a scenario-based method to foresight in the design and manufacturing industry

“…Authors used scenarios to produce visions for the robust planning exercise by analyzing future options (Bodwell and Chermack, 2010). Nonetheless, the robust planning process normally reflects a previous scenario analysis process, which means scenarios are really applied in the robust planning exercise (Popper, 2019). The method presented in this paper integrates the use of scenarios through the entire robust planning process as part of the GBN approach (Bishop et al , 2007; Meissner and Wulf, 2015; Millett, 2003; Schwartz, 2012; Vilkkumaa et al , 2018).…”