Decision dilemma: four core challenges of strategic game deduction methods

The world is currently experiencing unprecedented changes, with events such as the Ukraine conflict, the South China Sea dispute, and political differences in the Middle East exacerbating the intensity of local conflicts. Great power competition, such as the confrontation between Western countries and Russia, China and the United States, has led to local conflicts evolving into an extension of global power struggles. Against the backdrop of frequent geopolitical conflicts and normalized public crises, strategic game deduction has become a core tool for governments around the world to enhance their decision-making resilience. As an important means of pre practice, deduction is widely used in fields such as conflict analysis, military training, and crisis management. However, traditional deduction methods are not only expensive, but also have significant drawbacks:

Cognitive dilemma: 90% of major decisions rely on fragmented expert experience, like a blind man feeling an elephant.

Business intelligence interruption: There is a blind spot in the integration of dynamic data and domain knowledge, leading to a disruption in the continuity and value transmission of business intelligence.

Decision delay: The traditional deduction time and the speed of situation changes form a fatal scissors gap.

Trust crisis: The traditional AI inference process is opaque, making it difficult to reproduce the decision-making process and the decision results lack interpretability.

With the launch of the DeepSeek R1 inference model, it has demonstrated multiple breakthrough capabilities in the field of game deduction, driving multidimensional technological innovation and capability leaps. Specifically manifested as:

The ability to infer strategic intentions has significantly improved, enabling precise capture of core motivations in complex games;

The real-time intelligence fusion processing capability has been greatly improved, supporting rapid decision-making and response in dynamic environments;

Further optimization of scenario simulation and policy shock analysis capabilities, providing high-precision support for multi-dimensional inference.

Graph Model Hybrid: Driving the Cognitive Transition of Strategic Gaming from Data to Decision Making

The essence of strategic game theory is the competition of cognitive efficiency. Building an intelligent deduction system that is "autonomous, interpretable, iterative, and adversarial" has become a new focus of global technological competition. TRS fully utilized the technical advantages of DeepSeek R1 in reasoning large models and reconstructed the technical paradigm of game inference. By breaking through key technologies such as complex system analysis, social domain modeling, and intelligent processing of big data, an intelligent deduction system covering multiple fields such as politics, economy, and military has been constructed. It supports real-time analysis of major events, wartime deduction, and long-term planning, providing strong technical support for strategic research, crisis response, and high-level decision-making. TRS focuses on scenarios such as major event conflict deduction, mixed war simulation, and economic sanction effect simulation, promoting the industrialization process of technological achievements and assisting in the scientific and intelligent upgrading of national security and strategic decision-making. The construction of a strategic game deduction system based on the combination of event inference graph and large-scale model by TRS supports functions such as event situational awareness, event analysis, and game deduction.

In a certain project, TRS achieved technological breakthroughs in the following three aspects:

1. Construction of a decision-making knowledge system that combines reality and virtuality. A knowledge system for strategic game deduction has been constructed, including a real event library, a virtual event library, and a game theory graph, providing comprehensive knowledge support for event situation analysis, large-scale model optimization, and game deduction prediction.

2. Inferential auxiliary model based on the theory graph. Based on the game theory graph of virtual and reality, mining game measures with causal logic relationships, constructing a dataset of deduction task instructions, and fine-tuning and training the DeepSeek version of the TRS-GPT LLM to master professional knowledge in the field of deduction, forming a domain model for strategic games. Based on the optimized large model and combined with contextual information of the deduction scenario, provide users with accurate decision knowledge recommendations.

3. Structured discussions and decision optimization. By utilizing RAG (Retrieval Enhanced Generative) technology, internal strengths, weaknesses, and external opportunities and threats are automatically generated to assist users in quickly constructing SWOT matrices, providing structured evidence for decision-making knowledge generation and helping researchers efficiently clarify game thinking. Based on the recommended decision-making knowledge, a structured debate technique driven by large models is used to compare and analyze the advantages, disadvantages, and potential impacts of various solutions, ultimately selecting the most suitable solution and providing decision-making basis. This process simulates decision making and analysis in a game adversarial environment, significantly enhancing the scientific and interpretable nature of decision knowledge.

Practical case: closed-loop verification from theory to practice

To visually demonstrate the practical application value of graph model hybrid driven strategic game deduction in special industries, this section takes the ownership dispute of Greenland as an example to demonstrate the significant effectiveness of the TRS intelligent deduction system. The following is the deduction game analysis process proposed by TRS:

1. Derive scene settings

Recently, US President elect Trump has repeatedly proposed the expansion of the US territory, including the demand to acquire the Greenland Autonomous Okrug of Denmark. Despite repeated denials from officials in Denmark and Greenland that the island would be sold, Trump's actions undoubtedly once again drew global attention to this frozen island. In a post announcing the nomination of Ken Howe, a well-known science and technology entrepreneur and co-founder of PayPal and Founders Fund, as the US Ambassador to Denmark, Trump used the so-called "national security" as an excuse, claiming that "it is absolutely necessary for the United States to own and control Greenland".

2. Auxiliary analysis of strategic decision-making actions

Blue side: United States

Red side: Denmark

3. Assist in generating strategic decision-making actions for the Danish side

4. Expert+Large Model Selection of Danish Decision Action Measures

Experts evaluate the action measures based on the viewpoints presented by both sides in the red blue debate

Red side viewpoint: The Danish government may engage in formal or informal dialogue with the US government to understand the specific intentions of the US and express Denmark's position. This kind of dialogue may involve multiple levels such as diplomacy, economy, and security.

Blue side view: In response to the United States' claims, the Danish government may consider adjusting relevant laws and policies to further clarify and strengthen its sovereignty over Greenland. This may include measures such as strengthening border controls and increasing military presence.

Optimal selection of decision-making action measures for the Danish side based on a large model:

After three rounds of automatic debate in the large model, based on the final score of the debate, the "affirmative" viewpoint is selected as the preferred result of this measure.

This case fully demonstrates that the strategic game deduction system driven by graph model hybrid can effectively assist all parties in decision-making confrontation and deduction, generate accurate decision-making knowledge in crisis scenarios, and propose measures that are both reasonable and feasible. Based on structured research, measures optimization and evidence generation technology, through automatic domain debate, highly reproduces the expert decision-making process, ensuring the scientificity and credibility of the decision-making basis.

The DeepSeek version of the TRS-GPT LLM has shown significant results in major event game deduction, but in order to further enhance its application value, TRS is actively exploring future improvement directions, including:

Deeply integrate the company's open-source intelligence data to enrich the knowledge system construction of strategic game deduction.

Strengthen specialized fine-tuning in military, political, and government fields to enhance the accuracy and reliability of models in handling professional issues and data;

Enhance the functions of referencing and checking the thought chain, further improving the interpretability and decision-making credibility of the model.

Through the verification of this project, the TRS-GPT LLM combined with the company's data advantages in the field of open source intelligence, is expected to be more widely applied in more special industries, such as predicting the escalation path of international crises, evaluating the chain reaction of national intervention, joint action planning deduction, and strategic deterrence capability evaluation prospects.

Looking ahead to the future, TRS will accelerate the construction of a cross disciplinary professional think tank ecosystem, and promote the release of greater value of TRS-GPT LLM in a wider range of application scenarios through collaborative construction：

Politics: International major events, crisis management and conflict resolution, simulation of international relations and diplomatic negotiations, etc