Table 1. Summary of debiasing techniques... | Emerald Publishing

Table 1.

Summary of debiasing techniques applicable to the four behavioral biases covered in this paper and categorized by organizational, debate and analytical

Biases	Organizational techniques	Debate techniques	Analytical techniques
Confirmation bias	Two-level governance – A governance structure to challenge the decision team’s investment decisions. Having two layers of decision helps catch flawed judgments that make it past the decision team (Sibony et al., 2017)	Red team/blue team – Two separate teams develop competing recommendations on a proposal. One group – a blue team – investigates with a view to proposing the project or investment, while another – the red team – builds a case against the project or investment. The independent decision-maker decides based on the opposing cases presented (Heiligtag et al., 2017; Zhang and Gronvall, 2020). Mandatory alternatives – A rule whereby every team or individual proposing a project for approval is required to propose not one, but two. As a result “yes/no” or “whether or not” questions are outlawed and it becomes normal, not exceptional, to see projects being rejected (Sibony, 2020). Advance checklist “What would need to be true” – In advance of the facts of the proposal being known and discussed the decision-makers agree on the criteria they would require to be true in order for the decision to be made (McGrath and MacMillan, 2009)	Qualitative scenario analysis – Informs decisions by developing a set of qualitative, representative scenarios of alternative futures and identifying the likely consequences of the decision under consideration (Clemons, 1995)
Over-optimism	Mediating assessment protocol – Incorporates reference class forecasting and other debiasing practices, such as postponing the use of intuition, using relative scales, and benefiting from the wisdom of the crowd (Kahneman et al., 2019)	Premortem – The leader asks the group to imagine themselves in the future in which the project they are considering has been a total failure. The group is asked to consider the reasons that it went wrong. This serves to elicit weaknesses and risks (Klein, 2007). Consider the opposite/“What if we’re wrong” – Consideration of alternative hypotheses by asking “What are some reasons that our initial judgment might be wrong?” (Larrick, 2004)	Test, learn, adapt – A systematic policy of piloting concepts in advance of full roll-out. Instead of trying to predict the future, test a potential solution actively by trying it on a small scale (Reis, 2011; Smit and Lovallo, 2014). Periphery scan – Include learning from the past (e.g. What have been our past blind spots? What is happening in these areas now?), examining the present (e.g. What are your mavericks and outliers trying to tell you?) and envisioning new futures (e.g. What emerging technologies could change the game?) (Day and Schoemaker, 2005)
Anchoring/inertia	CEO piggybank – An approach to budgeting in which a large contingency fund is set aside to seize opportunities, whether to nurture existing businesses with additional capital or to acquire new assets at knockdown prices (Bradley et al., 2018; Lovallo et al., 2020)	If this was your money – An exercise in which each participant is asked to allocate funds, assuming this is their individual portfolio, not corporate funds. Reanchoring – Debate on cases where there is a large discrepancy between history (i.e. this year’s target) and model, and to allow a discussion in which large amounts are reallocated. Done using an outside set of forecasts (e.g. competitor benchmarking) (Lovallo and Sibony, 2012)	Inertia benchmarking – Measures the correlation between the percentage of resources each cell (e.g. division) in a portfolio received in the most recent year and what it received in previous years. This draws attention to whether resource allocation is too stable (Hall et al., 2012)
Planning fallacy	Trip-wires – Development of an early warning system that triggers one to act when certain pre-defined conditions are met (Soll et al., 2015a). Incentives/motivation – Establishes financial and non-financial reward policies for an accurate estimate of project and also establishes punishments for inaccuracies (Flyvbjerg, 2009). Share financial responsibility – Budget, cost over-runs and benefit shortfalls are shared between proposing and approving agencies. This reduces the agency problem driver of the planning fallacy (Flyvbjerg et al., 2009)	Additional downside – The rule of thumb to apply this approach is to “Add 20%–25% more downside to the most pessimistic scenario” and then decide whether the plan is still viable (Belsky and Gilovich, 2010). Unpacking a task – An exercise in which participants break down multifaceted tasks into precise subcomponents. Unpacking helps to consider under-counted components, and will provide a longer and more accurate forecast (Kruger and Evans, 2004)	Reference class forecast – A method of forecasting based on a sample of relevant comparable cases. Requires explicitly creating a large enough “reference class” (often from the experiences of other companies) (Lovallo and Kahneman, 2003). Similarity-based forecasting – An application of Reference Class Forecast where reference classes are not weighted equally but by similarity (Lovallo et al., 2012)

Biases	Organizational techniques	Debate techniques	Analytical techniques
Confirmation bias	Two-level governance – A governance structure to challenge the decision team’s investment decisions. Having two layers of decision helps catch flawed judgments that make it past the decision team (Sibony et al., 2017)	Red team/blue team – Two separate teams develop competing recommendations on a proposal. One group – a blue team – investigates with a view to proposing the project or investment, while another – the red team – builds a case against the project or investment. The independent decision-maker decides based on the opposing cases presented (Heiligtag et al., 2017; Zhang and Gronvall, 2020).Mandatory alternatives – A rule whereby every team or individual proposing a project for approval is required to propose not one, but two. As a result “yes/no” or “whether or not” questions are outlawed and it becomes normal, not exceptional, to see projects being rejected (Sibony, 2020).Advance checklist “What would need to be true” – In advance of the facts of the proposal being known and discussed the decision-makers agree on the criteria they would require to be true in order for the decision to be made (McGrath and MacMillan, 2009)	Qualitative scenario analysis – Informs decisions by developing a set of qualitative, representative scenarios of alternative futures and identifying the likely consequences of the decision under consideration (Clemons, 1995)
Over-optimism	Mediating assessment protocol – Incorporates reference class forecasting and other debiasing practices, such as postponing the use of intuition, using relative scales, and benefiting from the wisdom of the crowd (Kahneman et al., 2019)	Premortem – The leader asks the group to imagine themselves in the future in which the project they are considering has been a total failure. The group is asked to consider the reasons that it went wrong. This serves to elicit weaknesses and risks (Klein, 2007).Consider the opposite/“What if we’re wrong” – Consideration of alternative hypotheses by asking “What are some reasons that our initial judgment might be wrong?” (Larrick, 2004)	Test, learn, adapt – A systematic policy of piloting concepts in advance of full roll-out. Instead of trying to predict the future, test a potential solution actively by trying it on a small scale (Reis, 2011; Smit and Lovallo, 2014).Periphery scan – Include learning from the past (e.g. What have been our past blind spots? What is happening in these areas now?), examining the present (e.g. What are your mavericks and outliers trying to tell you?) and envisioning new futures (e.g. What emerging technologies could change the game?) (Day and Schoemaker, 2005)
Anchoring/inertia	CEO piggybank – An approach to budgeting in which a large contingency fund is set aside to seize opportunities, whether to nurture existing businesses with additional capital or to acquire new assets at knockdown prices (Bradley et al., 2018; Lovallo et al., 2020)	If this was your money – An exercise in which each participant is asked to allocate funds, assuming this is their individual portfolio, not corporate funds.Reanchoring – Debate on cases where there is a large discrepancy between history (i.e. this year’s target) and model, and to allow a discussion in which large amounts are reallocated. Done using an outside set of forecasts (e.g. competitor benchmarking) (Lovallo and Sibony, 2012)	Inertia benchmarking – Measures the correlation between the percentage of resources each cell (e.g. division) in a portfolio received in the most recent year and what it received in previous years. This draws attention to whether resource allocation is too stable (Hall et al., 2012)
Planning fallacy	Trip-wires – Development of an early warning system that triggers one to act when certain pre-defined conditions are met (Soll et al., 2015a).Incentives/motivation – Establishes financial and non-financial reward policies for an accurate estimate of project and also establishes punishments for inaccuracies (Flyvbjerg, 2009).Share financial responsibility – Budget, cost over-runs and benefit shortfalls are shared between proposing and approving agencies. This reduces the agency problem driver of the planning fallacy (Flyvbjerg et al., 2009)	Additional downside – The rule of thumb to apply this approach is to “Add 20%–25% more downside to the most pessimistic scenario” and then decide whether the plan is still viable (Belsky and Gilovich, 2010).Unpacking a task – An exercise in which participants break down multifaceted tasks into precise subcomponents. Unpacking helps to consider under-counted components, and will provide a longer and more accurate forecast (Kruger and Evans, 2004)	Reference class forecast – A method of forecasting based on a sample of relevant comparable cases. Requires explicitly creating a large enough “reference class” (often from the experiences of other companies) (Lovallo and Kahneman, 2003).Similarity-based forecasting – An application of Reference Class Forecast where reference classes are not weighted equally but by similarity (Lovallo et al., 2012)