Models of complex adaptive systems in strategy and organization research

Baumann, Oliver

doi:10.1007/s11299-015-0168-x

Cited by 17 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A second set of reviews deals with the models that underlie research on complexity and search. Baumann (2015) and Ganco and Hoetker (2009) provide more technical summaries of the NK model, including its assumptions and underlying mechanisms. Porter and Siggelkow (2008) provide an overview of the NK model and the complementarity framework from economics as two modeling approaches to capture interdependencies.…”

Section: More Broadly We Argue That Important Insights Can Be Gainedmentioning

confidence: 99%

Effective Search in Rugged Performance Landscapes: A Review and Outlook

Baumann

Schmidt

Stieglitz

2018

Journal of Management

Self Cite

113

View full text Add to dashboard Cite

The creation of novel strategies, the pursuit of entrepreneurial opportunities, and the development of new technologies, capabilities, products, or business models all involve solving complex problems that require making a large number of highly interdependent choices. The challenge that complex problems pose to boundedly rational managers—the need to find a high-performing combination of interdependent choices—is akin to identifying a high peak on a rugged performance “landscape” that managers must discover through sequential search. Building on the NK model that Levinthal introduced into the management literature in 1997, scholars have used simulation methods to construct performance landscapes and examine various aspects of effective search processes. We review this literature to identify common themes and mechanisms that may be relevant in different managerial contexts. Based on a systematic analysis of 71 simulation studies published in leading management journals since 1997, we identify six themes: learning modes, problem decomposition, cognitive representations, temporal dynamics, distributed search, and search under competition. We explain the mechanisms behind the results and map all of the simulation articles to the themes. In addition, we provide an overview of relevant empirical studies and discuss how empirical and formal work can be fruitfully combined. Our review is of particular relevance for scholars in strategy, entrepreneurship, or innovation who conduct empirical research and apply a process lens. More broadly, we argue that important insights can be gained by linking the notion of search in rugged performance landscapes to practitioner-oriented practices and frameworks, such as lean startup or design thinking.

show abstract

Section: More Broadly We Argue That Important Insights Can Be Gainedmentioning

confidence: 99%

Effective Search in Rugged Performance Landscapes: A Review and Outlook

Baumann

Schmidt

Stieglitz

2018

Journal of Management

Self Cite

113

View full text Add to dashboard Cite

show abstract

“…While computer scientists are primarily interested in finding the optimal solution to learning problems, organization scientists have focused on the descriptive value of learning models. In particular, learning problems in organizations have often been described within the learning by doing framework and modelled using reinforcement learning tasks (e.g., [11,[24][25][26][27][28][29]; see [30] for a review). is is because organizational learning problems frequently meet the two conditions that define reinforcement learning problems.…”

Section: Learning By Doing As a Form Of Reinforcement Learningmentioning

confidence: 99%

Self-Confirming Biased Beliefs in Organizational “Learning by Doing”

Park

Puranam

2021

Complexity

View full text Add to dashboard Cite

Learning by doing, a change in beliefs (and consequently behaviour) due to experience, is crucial to the adaptive behaviours of organizations as well as the individuals that inhabit them. In this review paper, we summarise different pathologies of learning noted in past literature using a common underlying mechanism based on self-confirming biased beliefs. These are inaccurate beliefs about the environment that are self-confirming because acting upon these beliefs prevents their falsification. We provide a formal definition for self-confirming biased beliefs as an attractor that can lock learning by doing systems into suboptimal actions and provide illustrations based on simulations. We then compare and distinguish self-confirming biased beliefs from other related theoretical constructs, including confirmation bias, self-fulfilling prophecies, and sticking points, and underscore that self-confirming biased beliefs underlie inefficient self-confirming equilibria and hot-stove effects. Lastly, we highlight two fundamental ways to escape self-confirming biased beliefs: taking actions inconsistent with beliefs (i.e., exploration) and getting information on unchosen actions (i.e., counterfactuals).

show abstract

“…Based on the work of Benz (2007) and Heinze and Kuhlmann (2007), various institutes conducting research in different fields are found to need a balance between cooperation and competition with other partner institutes under the same umbrella and with external research institutes. Additionally, there is another typical mediation situation at L2 (March, 1991;Baumann, 2015)-the choices between exploration of new research directions, institutes, programmes, and exploitation of the current resources. All the above hypothetical tensions are explored in the case studies of MPG, HGF, and CAS.…”

Section: The Enriched Conceptual Framework On the Role Of Evaluation mentioning

confidence: 99%

The balancing role of evaluation mechanisms: cases of publicity funded research institutions: MPG, HGF, and CAS

Luo

View full text Add to dashboard Cite

Models of complex adaptive systems in strategy and organization research

Cited by 17 publications

References 50 publications

Effective Search in Rugged Performance Landscapes: A Review and Outlook

Effective Search in Rugged Performance Landscapes: A Review and Outlook

Self-Confirming Biased Beliefs in Organizational “Learning by Doing”

The balancing role of evaluation mechanisms: cases of publicity funded research institutions: MPG, HGF, and CAS

Contact Info

Product

Resources

About