Decision and Control: The Meaning of Operational Research and Management Cybernetics.

Kennedy, Miles; Beer, Stafford

doi:10.2307/2391462

Cited by 76 publications

(11 citation statements)

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“…They were described in the systems-inspired design sciences of the 1960s mostly without the use of mathematically formalized models. In contrast, some of the political science and management applications of cybernetics were already more sophisticated in this (see the appendix to the 1966 edition of Deutsch, 1953 andBeer, 1966). Management cyberneticist Stafford Beer writes about design and designed systems (Beer, 1974, p. 42):…”

Section: Intersections With the Cybernetics Threadmentioning

confidence: 99%

Feedback thought at the intersection of systems and design science

de Oliveira,

Guzzo,

Pigosso

2024

Proc. Des. Soc.

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This paper explores the interplay of feedback principles in design and systems science. From their roots in engineering, biology, and economics, it investigates intersections between design, cybernetics and servomechanisms. The synthesis emphasizes the need for considering feedback in anticipating unintended consequences and proposes an integrative view reconciling fundamental assumptions from the different fields through simulation. This holistic approach underscores the pivotal role of feedback in understanding and addressing complex phenomena, such as rebound effects, in design science.

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Section: Intersections With the Cybernetics Threadmentioning

confidence: 99%

Feedback thought at the intersection of systems and design science

de Oliveira,

Guzzo,

Pigosso

2024

Proc. Des. Soc.

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“…To cope with external complexity (Crawford et al, 2015), agents need to increase internal complexity. Put differently, complexity can only be solved by complexity (Ashby, 1956;Beer, 1994Beer, [1966).…”

Section: Complexitymentioning

confidence: 99%

Uncertainty decomposed: Understanding levels of contingency to enable effective decision-making

Grüner

2024

Strategic Management

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Background: Uncertainty is a common challenge in managerial decision-making, especially when it comes to predicting future states, establishing cause-effect relationships, and having knowledge about relevant variables. However, it is difficult to deliberately address different types of uncertainty by applying specific decision-making strategies and hence enable reduction of uncertainty due to overlapping definitions and conflicting operationalization of the uncertainty construct. Purpose: The paper aims to delineate types of uncertainty along their epistemological configurations in terms of specific knowledge contexts to enable choices of suitable strategies for specific decision-making situations. Study design/methodology/approach: A literature review revises and discusses concepts of (un)certainty based on (im)perfect information and objectively/subjectively available assemblages of knowledge. Findings/conclusions: The paper provides a framework that encompasses and differentiates configurations of available information and knowledge applicable to decision-making situations. In order to achieve construct clarity and to free the original concept of uncertainty from conflicting definitions and heterogeneous operationalizations, the umbrella term contingency is introduced. It encompasses all states of (im)perfect information and variations in their epistemological configurations. Finally, the presented epistemological framework delineates levels of contingency along specific qualities of available information. The identified and discussed levels of contingency are certainty, risk, uncertainty in the narrow sense (i.n.s.), complexity, ambiguity/equivocality, and isotropy/radical uncertainty. The delineated levels of contingency help to tailor decision-making situation to specific epistemological configurations and hence may serve as a starting point for concluding and developing appropriate strategies to reduce contingency. Limitations/future research: A holistic understanding how to deal with and solve contingency requires further research focusing on aligning levels of contingency with strategies for decision-making (algorithms, causation, effectuation, bricolage, improvisation, trial & error) by taking types of knowledge (structural, procedural, conceptual) and contextual factors (e.g. time, [origin of] resources) into account.

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“…Functions that must be implemented at each scale level to make them viable were analyzed to obtain a valid model structure (Beer, 1994a(Beer, , 1994b(Beer, , 2007. Those functions are: 1.…”

Section: Model Structure With Its Three Layersmentioning

confidence: 99%

The Hotdog Model – How Turn a Tier 1 Automotive Company Into an Agile Organization

Atzberger

Dethloff²

2023

Proc. Des. Soc.

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The more dynamic the markets become, i.e., the more surprises the markets have in store for companies, the more important it becomes for companies to react quickly to these changes in order to have market-driven and individual solutions. This is especially important for tier 1 suppliers, as competition is fierce and the customers, the OEMs, also have to adapt to the changing market and thus customer needs.In order to be best prepared for this, it is necessary to align one's own company in a flexible and responsive manner. In particular, the context-specific development and application of a framework as well as a model for the improvement of internal collaboration in companies is necessary.In this paper, exactly such a framework has been developed in several pilot projects within the framework of an agile transformation at a tier 1 supplier over a period of two years by means of participative action research.As a result, the conception and development of an individually scaled agile framework for a tier 1 supplier in the automotive industry, the AHEAD framework, is presented here. The Hotdog model is the central basis for the collaboration model, which is scalable to all company levels.

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Decision and Control: The Meaning of Operational Research and Management Cybernetics.

Cited by 76 publications

References 0 publications

Feedback thought at the intersection of systems and design science

Feedback thought at the intersection of systems and design science

Uncertainty decomposed: Understanding levels of contingency to enable effective decision-making

The Hotdog Model – How Turn a Tier 1 Automotive Company Into an Agile Organization

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