Deliverable 06 Worksheet: The Market Research Team Wo 045959

Deliverable 06 Worksheetthe Market Research Team Working On This Pro

The market research team working on this project creates this payoff matrix that represents the scaled values that customers give to the different levels of service and the corresponding payoffs for the telecom company: Customer Telecom Company Buy Don’t Buy Upgrade (2, , 1) Don’t Upgrade (3, , 1) You recognize that the payoff matrix is not the best way to analyze this scenario. You will construct a game tree to model the scenario and perform backwards induction to find the optimum strategy, explaining all of your reasoning along the way.

Paper For Above instruction

The scenario presented involves strategic decision-making by both customers and the telecom company, where a simple payoff matrix fails to capture the sequential and strategic nature of the interactions. Therefore, a game tree is necessary as it explicitly models the temporal order of moves, potential threats, and strategic considerations. Unlike a payoff matrix, which displays simultaneous payoffs without considering the sequence, a game tree visually represents the decision points, possible actions, and outcomes, allowing for a thorough analysis through backward induction.

In this case, the game involves the telecom company deciding whether to upgrade or not, and the customer deciding whether to buy or not. Since the company’s decision influences the customer’s subsequent choice, the game is sequential with the company moving first. The company’s initial move sets the stage for the customer’s response, making it essential to model the game as a sequential game with the company's move occurring at the outset.

Constructing the game tree begins with the telecom company's decision node, where it chooses to upgrade or not. Following this, the customer observes the company's choice and responds by either buying or not buying the service. The payoff matrix provides the numerical rewards for each outcome, but within the game tree, these payoffs are associated with the terminal nodes following each sequence of actions. Non-credible threats can emerge when a party threatens an action that they would not genuinely follow through with because it is not in their best interest once the game unfolds. For example, a threat to not upgrade if the company anticipates losing customer goodwill may be non-credible if, in the equilibrium, the company prefers to cooperate rather than threaten.

To refine the game tree, non-credible threats are removed by eliminating strategies that an actor would not rationally execute. This involves analyzing the off-equilibrium paths and ensuring that each threat or action is credible—meaning each player’s threats align with their best interests if the game reaches that point. The process of backward induction then begins at the terminal nodes, where the payoffs are evaluated to determine the optimal responses by the players.

The first step in backward induction involves examining the customer’s possible responses to the company's initial decision. For each of the company's choices, the customer will choose the action that maximizes their payoff. By calculating these best responses, we can determine the customer’s optimal response for each scenario. This step simplifies the game tree, replacing the customer’s decision nodes with their rational responses, which then inform the company's subsequent best strategic move.

After identifying the customer’s best responses, the game tree is redrawn to reflect these strategic choices. The next step involves the company considering the revised game tree and selecting its decision (to upgrade or not) based on the anticipated responses of the customer. This iterative process continues, with each step of backward induction narrowing the set of credible strategies for both players.

The subsequent steps of backward induction ultimately lead to identifying the subgame perfect equilibrium—those strategies that are credible and optimal at every decision node. The optimal strategy for the telecom company may involve choosing to upgrade or not, depending on which action yields the highest expected payoff considering the customer’s rational response. Similarly, the customer’s optimal response will be the action that maximizes their payoff given the company’s initial move.

In conclusion, utilizing a game tree provides a more detailed and strategic understanding of the interaction between the telecom company and customers than a payoff matrix. Through backward induction, the analysis ensures that the resulting strategies are credible and optimal, facilitating better decision-making for the company and a clearer understanding of the customer’s likely behavior.

References

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