Understanding game theory is just a prerequisite. The first step towards strategic success is to use it to make an informed decision.
In the dynamics of a game, there come certain points where you have to take specific actions, which are followed by reactions, engaging players in a continuous loop of push and pull in an effort to tip the scales in their favor, hoping to get most or all of the pie.
So, in a way, the success of your endeavor depends on others’ moves as much as it depends on yours. Because there is no competition with just one participant. No matter what sort of competition you are engaged in, using game theory helps you shift from reactive to proactive action.
This is not the first time game theory has been used for decision-making. You can see a similar framework in almost every GT implementation for strategic decision-making. However, there are a few notable differences: most frameworks consider the players as rational or might have serious mathematical calculations.
While there is nothing wrong with these, it clearly depends on the requirements and the field you are implementing them in. I have made a few adjustments and kept it simple for easy implementation and enable quick decision making.
Step 1: Mapping the Human Matrix
Identify the participants or players of the game. They are the entities, either individuals or nations, whose moves have a direct impact on others’ situations and the outcome of the game.
Identifying the players is not enough. You have to understand them and their motivations. It helps give you insight into what drives them and to what extent they will go or what means they will take to achieve their objectives.
Of course, identifying their objective is also part of it. Because not everyone has the same definition of success. For example, in a market, while some businesses strive for immediate profits, some make moves to gain more market share.
In game theory, people often consider every player to make rational decisions but they fail because that’s not always the case. Often, people do tend to make irrational decisions. However, those decisions only seem irrational because you don’t know the logic behind them.
It is important to view the game not just from your POV but from other players’ POV as well. Knowing their motivation would help make sense of others’ decisions as well.
Step 2: Categorizing the Conflict
Figure out what kind of game you are playing. Is it a one-time face-off or a repeated game? Even in a one-time face-off, it can be a simultaneous move or a sequential move game. Knowing the nature of the game helps strategize your future plan of action.
For example, bidding for a project tender is a simultaneous move, one-shot game, while bidding for an auction is a sequential move, one-shot game. Price wars in the market are a sequential move, a repeated game, just like a fight between couples over doing chores. It’s a long-term game, so it’s necessary to be kind and cooperative.
Step 3: Finding Equilibrium
Equilibrium can differ from game to game. Generally, a Nash equilibrium is a “no point of regret” for all parties involved. For others, it’s about predicting the outcomes once the players have played their hands and nothing remains to play.
In business and war, it’s like predicting the outcomes based on various strategies that all involved parties would use, and now neither can win or gain more just by changing their strategy.
Finding equilibrium is a strategic need that allows you to avoid dead ends. This helps save valuable resources from being wasted in the wrong place. Like any AI-LLM development company, fighting regulators to be able to use the data the way they want. The result coming in their favor is highly unlikely.
If they realize this beforehand, then they could stop wasting money in legal battles and start using resources to collaborate with regulators to come up with policies and frameworks that would be beneficial to both the public and companies. Many companies have taken the latter route and have benefited exponentially.
Step 4: Information Asymmetry
Like a single player doesn’t hold all the cards in a game of cards, one does not have all the information regarding players, games, and the rules. Players can only see the moves made by other players, and not their intentions or hidden agendas. The same is true for other parties regarding your data. This is called the fog of war.
During a competition, most people are so focused on planning and making the next moves that they rarely pay any attention to what their competitors are doing. Even in any case, they do thorough research, it is impossible to extract or gain all the information.
For example, a market can make a sudden shift either due to the latest innovation or changed customer behavior. Even the most well-researched and properly marketed products, those which were anticipated to be the next big hit, fail.
There can be many reasons behind such a failure. But information asymmetry certainly plays a role in all of them. And that’s what most people fail to consider. But that’s not to discourage players from making a move. I’m not telling you not to move until you have it all pieced together or pave a perfect way. I’m telling you to consider that your assumptions and information may not be as important as you thought.
Therefore, players have to make decisions based on the available information, but be ready to accept new information and adapt quickly. You have to make the most out of what you have, but also be willing to accept what you don’t and still figure out a way to work things out.
It’s not as complex as it sounds. Play to your strengths; if a weakness limits you from achieving your goals, adapt to move forward. One of the best examples of this is the outsourcing models that businesses use. Doing everything on their own is time-consuming, so businesses play to their strengths and outsource everything else to third parties to focus on their core operations.
The Strategic Decision Matrix
Use this framework to analyze any situation where your success depends on the choices of others.
1. The Player Map
Identify the motivations of everyone involved. People rarely act “irrationally”—they just have different goals than you do.
| Player | Core Objective | What is their “Dealbreaker”? |
| You | e.g., Growth, Profit, Stability | What is your ‘hard no’? |
| Opponent | e.g., Market share, Survival | What forces them to quit? |
| Silent Player | e.g., Regulators, Customers | What changes their behavior? |
2. The Payoff Matrix ($2 \times 2$)
Map the four possible outcomes of a single interaction. Assign a value from 1 (Worst) to 5 (Best) for both parties.
| They Cooperate | They Compete | |
| You Cooperate | ( , ) | ( , ) |
| You Compete | ( , ) | ( , ) |
How to read this: The first number in the bracket is your payoff; the second is theirs. Look for the Nash Equilibrium: the box where neither player has an incentive to change their move if they know what the other is doing.
3. The Strategy Audit
Once the matrix is filled, run your decision through these three timeless filters:
- Is this a “One-Shot” or “Repeated” game?
In a one-shot game (like a one-time sale), players tend to be aggressive. In a repeated game (like a long-term partnership), the “Shadow of the Future” encourages cooperation because cheating today leads to retaliation tomorrow. - Do I have a “Dominant Strategy”?
Is there a move that is your best option regardless of what the other player chooses? If so, that is your logical path forward. - Can I change the game?
If the current Nash Equilibrium is a “lose-lose” (like a price war), the best move isn’t to play better—it’s to change the rules. Can you introduce a new value proposition, a partnership, or a “credible commitment” that shifts the payoffs for everyone?
Conclusion: Changing the Game
This was just a little effort from my side in putting together the existing GT practices with the insights I derived while learning GT. It is important to note that, while it takes reality, irrationality, and imperfection into account along with the ignored factors like motivation and equilibrium as a tool to guide the path, like every other tool, this is also imperfect and incomplete.
Therefore, the outcomes entirely depend on how effectively this GT-based decision model is implemented, adjusted, and adapted to real-world scenarios.
Do you think this model can help you make crucial decisions in any of your real-world games?
Kushalam 