AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction
Abstract
:1. Introduction
Research Objectives
- Develop an AI-powered framework integrating machine learning (ML), risk modelling, and game theory to optimise bid pricing and strategies;
- Model bidding process for improved success rates by leveraging historical data, competitive dynamics, and uncertainty quantification for strategic bid optimisation;
- Evaluate the model’s effectiveness through a UK case study in the construction sector.
2. Literature Review
2.1. Overview of Traditional Bid-Pricing Methods in Construction
2.2. Key Concepts in Game Theory for Construction Bidding
Utility Function for Payoff Calculation
2.3. Game Theory Applications in Construction Bidding
2.4. The Role of AI in Addressing Uncertainty and Predictive Decision Making
Key AI Techniques for Addressing Uncertainty
2.5. Foundations of AI Applications
2.5.1. Cost Estimation Using Regression Models
2.5.2. Risk Modelling with Bayesian Networks
2.5.3. Time-Series Forecasting for Market Trends
2.5.4. Competitor Behaviour Prediction Using Classification Models
2.5.5. RL for Adaptive Bidding
2.6. Gaps in Current Research and Justification for the Study
2.6.1. Gaps in Current Research
2.6.2. Justification for the Study
3. Methodology
Case Study and the Assisted Game Theory Model
4. Results and Discussion
5. Conclusions and Future Work
5.1. Limitations of the Study
5.2. Future Research
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature Name | Description | Why It Matters |
---|---|---|
Material Cost | Total cost of raw materials | Major component of total project cost |
Labour Cost | Total worker wages for the project | Highly variable, depends on location and project type |
Project Complexity | Scale of difficulty (low/medium/high) | Complex projects require more time and resources |
Market Demand | Indicator of project demand | High demand raises bid prices |
Competitor Win Rate | % of past bids won by competitors | Helps AI predict aggressive/conservative bidding |
Economic Index | Captures inflation, GDP, and bidding environment | Affects material and labour prices |
Seasonality Factor | Whether the project occurs during the peak or off-peak season | Cost fluctuations due to supply/demand cycles |
Feature | Mean Value (UK Market) |
---|---|
Material Cost | GPB 150,000–600,000 |
Labour Cost | GPB 100,000–350,000 |
Project Complexity Score | 1 (Low)–5 (High) |
Economic Index (UK Inflation) | 105 (Base = 100) |
Competitor Aggressiveness Score | 0.65 (More Aggressive) |
Feature | Description |
---|---|
Material Cost | Cost of raw materials (steel, cement, etc.) |
Labour Cost | Total wages for workers |
Project Complexity | A numerical rating of project difficulty |
Market Demand | Indicator of how much demand exists for the project type |
Economic Index | Captures inflation and market stability |
Competitor Behaviour | Bidding patterns of rival firms |
Seasonality Factor | Cost variations due to seasonal trends |
Feature | Description |
---|---|
Past Bid Price | The competitor’s last bid amount |
Market Demand | Economic indicators affecting project bidding |
Competitor Market Position | Whether the firm is small, medium, or large |
Past Win Rate | Percentage of bids won by the competitor |
Project Complexity | Higher complexity may influence bidding behaviour |
Economic Index | Captures inflation, interest rates, and market stability |
Statistic | Value (GBP) |
---|---|
Predicted Cost | 2.85 M |
MAE | 100 K |
R² Score | 0.92 |
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Serugga, J. AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction. AppliedMath 2025, 5, 39. https://doi.org/10.3390/appliedmath5020039
Serugga J. AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction. AppliedMath. 2025; 5(2):39. https://doi.org/10.3390/appliedmath5020039
Chicago/Turabian StyleSerugga, Joas. 2025. "AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction" AppliedMath 5, no. 2: 39. https://doi.org/10.3390/appliedmath5020039
APA StyleSerugga, J. (2025). AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction. AppliedMath, 5(2), 39. https://doi.org/10.3390/appliedmath5020039