Artificial Intelligence–Based Decision Support in Cryptocurrency Markets: A Game-Theoretic & Probabilistic Model

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Over the past decade, cryptocurrency markets have emerged as one of the most disruptive innovations in the financial world, attracting attention not only for their high return potential but also for the profound risks they entail. Unlike traditional financial assets, cryptocurrencies are highly influenced not only by supply-demand dynamics but also by social media trends, technological updates, and regulatory news due to their decentralized structure and 24/7 trading nature. This challenges the validity of classical economic theories that assume rational market participants.

Although artificial intelligence offers new perspectives in understanding human behavior and market dynamics, existing approaches are often limited to “black-box” models and lack sufficient adaptability to sudden market shocks.

Most quantitative finance models and basic machine learning algorithms assume that historical patterns will repeat in the future. However, cryptocurrency markets are chaotic and non-stationary. Two key problems emerge in this context:

1. Current algorithms struggle to model “irrational” market movements.
2. Deep learning models lack explainability, making it difficult for humans to understand the reasoning behind buy/sell decisions.

These limitations undermine investor trust in AI-based systems and slow technological adoption.

This paper contributes by:

Developing a hybrid probabilistic model based on Dempster–Shafer belief theory, which evaluates uncertainty through evidence weighting rather than purely Bayesian probability.

Proposing a simulation framework that models market dynamics as a multi-agent game, enabling creative problem-solving within interactive environments.

2. Related Work

The proposed approach is grounded in three primary research domains: AI in economic theory, game-based simulations, and probabilistic reasoning.

2.1 AI and Economic Theory

The rise of AI has significantly influenced economic theory. While traditional models rely on the “Homo Economicus” assumption of rational agents, modern AI approaches better capture bounded rationality and asymmetric information. Concepts such as supply-demand equilibrium, pricing, and game theory have been enhanced through computational techniques inspired by natural intelligence.

This study builds upon this transformation to interpret irrational pricing dynamics in crypto markets.

2.2 Simulation Environments and Game Theory

Games provide controlled environments for studying decision-making under uncertainty. Cryptocurrency trading can be modeled as a complex, non-zero-sum game where multiple agents compete and interact.

Although game simulations are widely used in agent training, their adaptation to financial market simulations—especially for generating creative strategies—remains an emerging field. This research utilizes game-based environments as testing grounds to improve agent performance in market simulations.

2.3 Probabilistic Judgment and Uncertainty Management

AI systems typically rely on Bayesian theory or belief function theory to handle probabilistic reasoning. While Bayesian theory assigns precise numerical probabilities, Dempster–Shafer theory emphasizes evidence strength and uncertainty modeling.

In noisy and incomplete data environments such as crypto markets, belief functions provide a more flexible structure for representing unknowns. This study adopts a non-Bayesian perspective to assess the reliability of market signals.

3. Methodology and Approach

The proposed system is called the Crypto-Game-Belief Framework. It consists of modular components that process market data, manage uncertainty, and develop strategies in simulated environments.

3.1 Core Components

Data Perception and Belief Formation Module

The system collects raw data such as price movements, trading volume, and social media sentiment. Instead of generating direct buy/sell signals, these inputs are converted into belief masses using Dempster–Shafer theory.

For example, a bullish technical indicator becomes a weighted piece of evidence rather than a fixed probability. This allows the system to model indecision when facing contradictory signals.

Creative Problem Solving (CPS) and Anomaly Management

Crypto markets frequently experience unprecedented events. Autonomous systems require Creative Problem Solving (CPS) capabilities to handle such non-nominal situations.

This module enables adaptive reasoning beyond memorized patterns, generating logical strategies in unfamiliar contexts.

Multi-Agent Game Simulation

Belief outputs feed into a game-based simulation environment. The AI agent competes against other virtual agents representing diverse trading strategies.

Using reinforcement learning, the agent maximizes a reward function while testing strategies without real-world financial risk.

3.2 Evaluation Plan

A hypothetical evaluation framework includes:

Dataset: Hourly BTC and ETH data (2018–2023) plus social media metrics.

Benchmarks: Buy-and-Hold strategy and a standard LSTM neural network.

Metrics:

Return on Investment (ROI)

Sharpe Ratio

Maximum Drawdown

The system is trained on 70% of the data and tested on 30%. Artificial “black swan” scenarios (e.g., sudden 20% drops) are introduced to test CPS adaptability.

4. Discussion

4.1 Practical Applications and Trust

The framework can serve not only as an automated trading tool but also as a risk management assistant for institutional investors. However, user trust depends heavily on explainability.

Explainable AI (XAI) research shows that understandable reasoning significantly improves trust. Therefore, instead of merely issuing a “Sell” signal, the system should provide contextual explanations such as:

“Market uncertainty has reached 80% according to belief functions; risk exposure is being reduced.”

4.2 Limitations Computational Cost: Multi-agent simulations and belief updates require significant computational power, which may potentially cause latency. Historical Bias: Artificial intelligence remains constrained by the historical patterns on which it is trained. Human Psychological Complexity: Modeling subjective human probability judgments remains challenging.

4.3 Ethical Considerations The deployment of artificial intelligence in crypto markets carries risks of manipulation. Large-scale algorithmic actors may exploit market signals in unethical ways. Financial AI systems must adhere to principles of transparency, fairness, and non-maleficence.

4.4 Future Work Future research may integrate Large Language Models (LLMs) to automatically analyze news and academic literature. Additionally, cross-referenced AI ethics frameworks could support the development of universal ethical standards for trading bots.

5. Conclusion Cryptocurrency markets represent a high-risk intersection of technology and finance. This study proposes a holistic AI framework that combines game theory, Dempster–Shafer belief functions, and creative problem-solving techniques. By modeling markets as dynamic interactive systems rather than static datasets, the framework enhances decision-making under uncertainty. Future iterations incorporating explainability features may foster a more transparent and trustworthy financial ecosystem for both individual and institutional investors.