In this paper an evaluation of the current situation regarding Evolutionary Algorithms (EA) in management applications is given. While assembling as many references as possible, the overview is, of course, not complete, but should give a representative account of current focuses of research and practical applications. EA-references are divided in three major categories: practical applications in business, applications-oriented research (mainly at universities), and abstract standard operations research problems with relevance beyond the domain of management.

An Overview of Evolutionary Algorithms in Management Applications

We extend agency theory to incorporate bounded rationality of both principals and agents. In this study we define a simple version of the principal-agent game and examine it using object-oriented computer simulation. Player learning is simulated with a genetic algorithm model. Our results show that players of incentive games in highly uncertain environments may take on defensive strategies. These defensive strategies lead to equilibria which are inferior to Nash equilibria. If agents are risk averse, the principal may not be able to provide enough monetary compensation to encourage them to take risks. But principals may be able to improve system performance by identifying good performers and facilitating information exchange among agents.

The principal-agent problem with evolutionary learning

An Investigation of the Nexus Between Strategic Planning and Organizational Learning

/pdf/evolutionary-learning-the-significance-for-business-1o5qm8ekze.pdf

Evolutionary Learning: The Significance for Business Organisation and Strategy

Cognitive capacity as competitive advantage: a simulation test

I propose that both individuals and organizations can learn strategies in complex or ill-defined situations more effectively, using the process of inductive versus deductive reasoning.
To support this proposition, a theory of strategy learning is developed, where a strategy consists of a set of moves which can be combined many ways to respond to business situations. Strategy learning is captured by three basic processes: variation, adaptation, and selection. My theory is implemented using a computer-simulated game among artificial agents, with each agent represented by a computer program capable of intelligent learning. This game can make more significant predictions for a reputation game than traditional game theory. Computer simulation results are then compared with results from corresponding economic experiments. They indicate that sophisticated strategies such as reputation building can be learned inductively.
Using computer simulation of the genetic algorithm, Part II explores different hierarchical levels of organizational evolution. In the case presented, selection at the population level of analysis influenced learning capabilities at the organizational level, while the learning capabilities influenced the selection process. Thus, it is crucial to understand how strategy learning at these different organizational levels interact with each other.
Part III discusses the importance of long chains of strategy moves. A mathematical model demonstrates that specific strategic instances can be identified effectively with a "bottom-up" hierarchical search, which first looks for the smallest, stable parts of an action chain and then combines them into larger and larger sub-strategies until the final strategy is identified. This model can predict how humans learn specific strategic instances when provided with intermediate feedback. Empirical evidence from a pilot experiment indicates that humans use hierarchical search to discover action chains.
This work provides a theoretical foundation for the evolution of intelligent organizations. If individuals can learn sophisticated strategies inductively, then so can organizations. Organizations should be able to function like large, parallel-distributed information systems, capable of learning strategies more effectively and able to leverage their strategic decision-making capabilities through the contribution of many intelligent individuals.

How strategies are learned

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michael D. Cohen

Author Tools

Chat about Author

Papers

How strategies are learned