Strategic Foresight Tools for Planning and Policy

2021 ◽  
pp. 775-797
Author(s):  
Barbara Jane Holland
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Good Predictor ◽  
Cognitive Biases ◽  
Strategic Thinking ◽  
Policy And Strategy ◽  
Strategic Foresight ◽  
Structured Approach ◽  
Blind Spots

Today, companies can no longer assume that the past will be a good predictor of the future; Those that fail to prepare for radically new possibilities may face sudden irrelevance. Strategic Foresight, aka, Futures thinking, provides a structured approach enabling people and organizations to overcome cognitive biases and think more realistically about change. It helps to uncover blind spots, imagine radically different futures, and improve decision-making. Climate disruption, artificial intelligence, and automation are quickly transforming the landscape for business and sustainability. This chapter will review the Strategic Foresight tools used to embed long-term strategic thinking and planning concerning policy and strategy.

scholarly journals How should we regulate artificial intelligence?

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170360 ◽  
Author(s):  
Chris Reed
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Current System ◽  
General System ◽  
Fundamental Rights ◽  
Ex Post ◽  
Ex Ante ◽  
Human Decision

Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

Das Potenzial der Strategischen Vorausschau zur Reduktion kognitiver Verzerrungen

2019 ◽  
Vol 60 (3) ◽  
pp. 19-22
Author(s):  
Marion A. Weissenberger-Eibl ◽  
Tamara Huber
Keyword(s):  
Information Processing ◽  
Cognitive Biases ◽  
Group Composition ◽  
Human Information Processing ◽  
Future Developments ◽  
Strategic Foresight ◽  
Human Thinking ◽  
World Information ◽  
The One

In order to secure a long-term competitive advantage in an increasingly complex world, information gathering, evaluation and exploitation is vital for uncovering future developments and dynamics in the corporate environment. The Strategic Foresight methods systematize the process of information processing, allowing a targeted look into the future. The benefits of such methods depend largely on the individuals who perform them. They may be subject to dysfunctional ways of thinking and behaving that evolves from mental models and the restricted ability of human information processing for coping with complexity and reflecting reality. On the one hand, the methods of Strategic Foresight contribute to the reduction of human dysfunctions, so called cognitive biases, by the approach design. On the other hand, the group composition of the employees involved and their degree of heterogeneity also have the potential to minimize biases. Applying approaches from cognitive science for human thinking in the field of Strategic Foresight outlines the contribution of foresight methods for reducing individual dysfunctions.

scholarly journals Initial evidence for biased decision-making despite human-centered AI explanations

2021 ◽  
Author(s):  
Nicolas Scharowski ◽  
Florian Brühlmann
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cognitive Biases ◽  
Ongoing Debate ◽  
Explainable Artificial Intelligence ◽  
Decision Making Processes ◽  
Financial Decision ◽  
Biases And Heuristics ◽  
User Trust ◽  
Initial Price

In explainable artificial intelligence (XAI) research, explainability is widely regarded as crucial for user trust in artificial intelligence (AI). However, empirical investigations of this assumption are still lacking. There are several proposals as to how explainability might be achieved and it is an ongoing debate what ramifications explanations actually have on humans. In our work-in-progress we explored two posthoc explanation approaches presented in natural language as a means for explainable AI. We examined the effects of human-centered explanations on trust behavior in a financial decision-making experiment (N = 387), captured by weight of advice (WOA). Results showed that AI explanations lead to higher trust behavior if participants were advised to decrease an initial price estimate. However, explanations had no effect if the AI recommended to increase the initial price estimate. We argue that these differences in trust behavior may be caused by cognitive biases and heuristics that people retain in their decision-making processes involving AI. So far, XAI has primarily focused on biased data and prejudice due to incorrect assumptions in the machine learning process. The implications of potential biases and heuristics that humans exhibit when being presented an explanation by AI have received little attention in the current XAI debate. Both researchers and practitioners need to be aware of such human biases and heuristics in order to develop truly human-centered AI.

Making intelligence more transparent

2016 ◽  
Vol 15 (1) ◽  
pp. 63-93
Author(s):  
Hui Zhang ◽  
Weichao Di
Keyword(s):  
Cognitive Biases ◽  
Strategic Thinking ◽  
Intelligence Community ◽  
Conceptual Blending ◽  
Cognitive Analysis ◽  
Critical Metaphor Analysis ◽  
Rising China ◽  
Language Strategies ◽  
Strategic Policies

This paper attempts to make a critical cognitive analysis of US strategic intelligence reports and aims at investigating language strategies and cognitive biases that occur in the reports and how the reports create the “realities” that may influence policymakers’ decisions. The data for analysis include seven reports released by US Intelligence Community which analyze, more or less, the Sino-US relations currently and in the foreseeable future as well as the implications of a rising China on US policies. The analytic framework is built up by integrating Critical Metaphor Analysis, Conceptual Blending Theory and Discourse Space Theory, each of which deal with different aspects of the discourse and on the whole provide a comprehensive analysis of it. The critical cognitive analysis of intelligence reports could disclose the views that analysts hold on particular issues, providing valuable reference for understanding or evaluating their reports’ contents, and reveal the ideology inherent in US strategic thinking, helping to estimate US strategic policies in the long term.

The Crusader Strategy

2020 ◽  
Author(s):  
Steve Tibble
Keyword(s):  
Decision Making ◽  
Strategic Thinking ◽  
Primary Sources ◽  
Patterns Of Behavior ◽  
Strategic Goals ◽  
Crusader States ◽  
Develop Strategy ◽  
Over Time ◽  
Meaningful Sense

Medieval states, and particularly crusader societies, often have been considered brutish and culturally isolated. It seems unlikely that they could develop “strategy” in any meaningful sense. However, the crusaders were actually highly organized in their thinking and their decision making was rarely random. This book draws on a rich array of primary sources to reassess events on the ground and patterns of behavior over time. The book shows how, from aggressive castle building to implementing a series of invasions of Egypt, crusader leaders tenaciously pursued long-term plans and devoted single-minded attention to clear strategic goals. Crusader states were permanently on the brink of destruction; resources were scarce and the penalties for failure severe. Intuitive strategic thinking, the book argues, was a necessity, not a luxury.

Township recycling performance assessment based on fuzzy TOPSIS model: An empirical investigation using the artificial intelligence-based VIKOR approach

2021 ◽  
pp. 1-7
Author(s):  
Kuo-Yan Wang
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Performance Assessment ◽  
Local Level ◽  
Evaluation Process ◽  
Fuzzy Topsis ◽  
Waste Reduction ◽  
Unit Pricing ◽  
Environmental Income

The problem of waste reduction is particularly emphasized the sources of environmental income i.e. tax and unit-pricing, instead of stimulus recycling administrative in the published literatures. A long-term rebate policy for recycling fund allowance was implemented in different metropolitan districts. However, evaluating the recycling performance at the local level does not include examining its rationality and efficiency in detail. To understand the uncertainty of role in decision making for substitute ranking, analysis based on decision making using fuzzy interval for performance assessment. This article redefines the criteria for evaluating recycling performance at the township level with assistance of artificial intelligence, and illustrates the results. We used a simple and swift evaluation process, namely, the VIKOR method, in place of the traditional public hearing or the Delphi method. The conclusion derived from the results can be used to analyze the effectiveness of the rebate policy for recycling at the township level.

Issues of Capacity and Decision-Making in the Context of Delirium

2015 ◽  
Vol 24 (4) ◽  
pp. 140-145
Author(s):  
Kevin R. Patterson
Keyword(s):  
Decision Making ◽  
Patient Autonomy ◽  
Care Setting ◽  
Cognitive Change ◽  
Treatment Team ◽  
Precipitating Factors ◽  
Term Care ◽  
Decision Making Capacity ◽  
Fundamental Consideration

Decision-making capacity is a fundamental consideration in working with patients in a clinical setting. One of the most common conditions affecting decision-making capacity in patients in the inpatient or long-term care setting is a form of acute, transient cognitive change known as delirium. A thorough understanding of delirium — how it can present, its predisposing and precipitating factors, and how it can be managed — will improve a speech-language pathologist's (SLPs) ability to make treatment recommendations, and to advise the treatment team on issues related to communication and patient autonomy.

Confirmation bias in decision making for fingerprints, DNA and eyewitness evidence cannot be explained by cognitive style or thinking dispositions.

2019 ◽  
Author(s):  
Daniel Edgcumbe
Keyword(s):  
Decision Making ◽  
Cognitive Style ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Criminal Case ◽  
Court Cases ◽  
Legal Proceedings ◽  
Thinking Dispositions ◽  
Evaluation Of Evidence ◽  
Facial Composite

Pre-existing beliefs about the background or guilt of a suspect can bias the subsequent evaluation of evidence for forensic examiners and lay people alike. This biasing effect, called the confirmation bias, has influenced legal proceedings in prominent court cases such as that of Brandon Mayfield. Today many forensic providers attempt to train their examiners against these cognitive biases. Nine hundred and forty-two participants read a fictional criminal case and received either neutral, incriminating or exonerating evidence (fingerprint, eyewitness, or DNA) before providing an initial rating of guilt. Participants then viewed ambiguous evidence (alibi, facial composite, handwriting sample or informant statement) before providing a final rating of guilt. Final guilt ratings were higher for all evidence conditions (neutral, incriminating or exonerating) following exposure to the ambiguous evidence. This provides evidence that the confirmation bias influences the evaluation of evidence.

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