| Variables and Payoffs with Uncertainty:
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| DF_M : Payoff when using data fabric in a monolithic architecture depends on the firm's revenue, costs, efficiency gains, and costs of data fabric, subject to uncertainty:
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| DF_M = R (revenue) - C (costs) + X (efficiency gain) - Y (costs of data fabric) + U (uncertainty factor)
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| DF_{MS} : Payoff when using data fabric in a microservices architecture considers revenue, costs, efficiency gains, costs of data fabric, and microservices transition, subject to uncertainty:
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| DF_{MS} = R (revenue) - C (costs) + Z (additional efficiency gain) - Y (costs of data fabric) - W (cost of microservices transition) + U (uncertainty factor)
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| NDF_M : Payoff for not using data fabric in a monolithic architecture takes into account revenue, costs, inefficiency costs, and uncertainty:
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| NDF_M = R (revenue) - C (costs) - M (inefficiency cost) + U (uncertainty factor)
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| NDF_{MS} : Payoff for not using data fabric in a microservices architecture accounts for revenue, costs, inefficiency costs, additional risk costs, and uncertainty:
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| NDF_{MS} = R (revenue) - C (costs) - M (inefficiency cost) - V (additional risk cost) + U (uncertainty factor)
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| TM : Payoff for transitioning to microservices considers revenue, costs, scalability gains, efficiency gains, disruption costs, transition costs, and uncertainty:
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| TM = R (revenue) - C (costs) + A (scalability gain) + B (efficiency gain) - D (disruption cost) - E (transition cost) - F (transition risk cost) + U (uncertainty factor)
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To achieve these goals, the integration of data fabric and microservices has emerged as a game-changing paradigm. Data fabric offers a holistic approach to data management, bridging the gap between diverse data sources and formats.
Microservices, on the other hand, advocate for decomposing complex applications into smaller, independent services.
Together, these concepts empower startups to create flexible, scalable ecosystems that leverage real-time data insights for innovation and growth.
With unified and real-time data insights, startups can make informed decisions, identify market trends, and streamline operations for growth.
Microservices revolutionize the architectural approach to software applications.
By decomposing complex systems into smaller, independently deployable services, startups gain improved scalability, fault tolerance, and faster development cycles. Integrating data fabric principles within a microservices architecture allows startups to leverage the power of data, adapt quickly to market shifts, and deliver exceptional user experiences.
Rounds
Payoffs
The payoff calculations are assumed to be deterministic, without any random elements or uncertainties.
Behavior
Information
Parameters
Transition
Improvement
c. Variables and Payoffs: The game involves several variables and associated payoffs, which capture the outcomes of each action and architecture.
The identified variables include $DF_M$, $DF_{MS}$, $NDF_M$, $NDF_{MS}$, and $TM$.
The payoffs for each action and architecture were defined based on revenue, costs, efficiency gains, risks, scalability gains, inefficiency costs, disruption costs, and transition costs.
d. Dynamic Programming Approach : To determine the optimal strategy for each firm at each round, a dynamic programming approach was employed.
The Bellman equation and backward induction were used to calculate the maximum cumulative payoff for each strategy at each round, starting from the last round and iterating backward.
This approach allows for strategic decision-making over multiple rounds, optimizing the firm's actions to maximize the cumulative payoff.
Game theory is the framework used to analyze startup efficiency for several reasons:
Objective: Maximize cumulative payoff over multiple rounds by selecting the optimal architecture strategy.
$V(S, t) = max [\sum P(S, a, S') \cdot (R(S, a, S') + V(S', t+1))]$
Where:
$P(S, a, S')$ is the probability of transitioning from state $S$ to state $S'$ when taking action a.
$R(S, a, S')$ is the expected reward when transitioning from state $S$ to state $S'$ when taking action a.
We apply game theory and dynamic programming to analyze the efficiency of startups in technology adoption and architectural decisions.
By modeling the decision-making process as a game, it offers a framework for assessing trade-offs and payoffs. The model considers adopting data fabric technology and transitioning to microservices, enabling startups to make informed choices for long-term benefits.
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