Information-Theoretic Cost-Benefit Analysis of Hybrid Decision Workflows in Finance
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new quantitative method for analyzing hybrid decision workflows in business and finance. It uses information-theoretic cost-benefit analysis to optimize data-informed processes and understand trade-offs.
Area Of Science
- Business Analytics
- Financial Management
- Information Theory
Background
- Data-driven decision-making is crucial for organizational growth in business and finance.
- Current methods lack a quantitative approach to analyze the cost-benefit of hybrid workflows.
- Determining trade-offs between human and machine processes and quantifying biases remains a challenge.
Purpose Of The Study
- To develop a methodology for analyzing hybrid decision workflows in business and finance.
- To translate information-theoretic concepts into a practical cost-benefit analysis framework.
- To quantitatively estimate the cost-benefit of individual processes within these workflows.
Main Methods
- Combined an information-theoretic approach with an engineering approach (workflow decomposition).
- Utilized information-theoretic measures for quantitative cost-benefit estimation.
- Applied the methodology through three distinct case studies.
Main Results
- Demonstrated the feasibility of the proposed information-theoretic cost-benefit analysis methodology.
- Case studies covered statistical algorithms, incomplete information with human knowledge, and cognitive biases.
- Provided a quantitative basis for analyzing hybrid decision workflows.
Conclusions
- The proposed methodology offers a systematic and quantitative approach to optimizing data-informed decision workflows.
- This work represents an early but significant step towards computer-assisted optimization in business and finance.
- Enables better understanding of trade-offs and biases in hybrid human-machine processes.
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