AI Strategic Modeling in Hospitality and Tourism: Intelligent Decision-Making from a Game Theory Perspective

This article focuses on AI strategic decision-making in the hospitality and tourism industry. Its core lies in using a game theory framework and MATLAB models to analyze the complex interactive relationships between AI adoption, pricing strategies, employee empowerment, and customer behavior, explore the strategy equilibrium issues among multiple stakeholders (hotels, employees, customers, etc.), and provide theoretical guidance for industry practice.

Intelligent Customer Service

• Intelligent chatbots: Handle booking inquiries, complaints, etc., 24/7 with personalized responses
• Voice assistants: In-room voice control of facilities and service requests
• Personalized recommendations: Customized travel routes and dining options based on user data

Operational Efficiency Improvement

• Dynamic pricing: Machine learning adjusts room rates in real-time to maximize revenue
• Demand forecasting: Optimize inventory management and staff scheduling
• Intelligent maintenance: IoT + AI predicts equipment failures

Robotic Services

• Delivery robots: Transport room supplies
• Cleaning robots: Automated cleaning of public areas
• Front desk robots: Handle check-in/check-out procedures

Game Theory Basics

• Core elements: Players (hotels, employees, customers, competitors, etc.), strategies, payoffs, equilibrium

Typical Game Scenarios in the Hospitality Industry

• Price competition (Bertrand model), technology adoption decisions, labor-management games, supply chain coordination

Three-Party Game Model Setup

• Hotels: Decide on AI adoption, investment level, pricing, and training input
• Employees: Choose to accept training, maintain the status quo, resist change, or leave
• Customers: Choose the proportion of AI services used and price/quality-sensitive options

Analyze strategy equilibrium results through MATLAB numerical simulations.

Factors Influencing AI Adoption

• Competitive pressure, cost-effectiveness, customer acceptance, employee skills and willingness

Evolution of Pricing Strategies

• Real-time response to competitive prices increases the risk of price wars
• Personalized pricing needs to balance fairness and revenue

U-Shaped Relationship with Employee Satisfaction

• Initial stage: Job insecurity leads to decreased satisfaction
• Transition stage: Adaptation after training and job restructuring
• Long-term: AI replaces repetitive tasks, allowing focus on high-value work to improve satisfaction

Gender Perspective

• Female-dominated positions (front desk, housekeeping) are prone to AI replacement; technical positions have a high male proportion, so gender equality needs attention.

Triple Bottom Line Framework

• Economic: Long-term returns rather than short-term cost reduction
• Social: Promote employee development rather than replacement
• Environmental: Optimize energy use and support environmental goals

Stakeholder Collaboration

• Internal: Collaboration between management, employees, and technical departments
• External: Cooperation with suppliers, OTAs, and industry associations
• Customers: Participate in AI service design and improvement

Recommendations for Managers

• Implement AI incrementally, expand after pilot testing
• Employee-centric: Invest in training to create human-AI collaboration roles
• Segment customers and provide differentiated services
• Establish data governance norms to protect privacy

Recommendations for Employees

• Lifelong learning of AI skills, view AI as an enhancement tool
• Explore new career paths (AI trainers, human-computer interaction designers)
• Protect rights and interests through labor unions

Recommendations for Policymakers

• Support employee retraining programs
• Develop AI ethical guidelines and regulatory standards
• Provide tax incentives and R&D subsidies to support AI innovation

Technological Trends

• Generative AI: More natural human-computer interaction
• Metaverse: Virtual tourism experiences
• Blockchain: Reconstruct relationships between hotels and OTAs

Research Methods

• Behavioral game theory: Combine experimental economics to study decision biases
• Multi-agent simulation: Simulate ecosystem dynamics
• Longitudinal case studies: Track AI transformation processes

The AI transformation in the hospitality industry is an exploration of human-AI collaboration models, requiring a balance between efficiency and humanity, cost and quality, short-term gains and long-term value. Technology is a means; the goal is to achieve better services, sustainable operations, and fair employment. The future hospitality industry will be a world where human warmth and machine efficiency coexist harmoniously.