Winning the Hardware Software Game Winning the Hardware-Software Game - 2nd Edition

Using Game Theory to Optimize the Pace of New Technology Adoption
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This is a new idea I'm working on. I'd love to hear any feedback you might have.

 

We Collect and Analyze Data

Why do we collect and analyze data? It informs us about (i) what happened in a given time and place and (ii) why (see Figure 1).

 

fig1

We use that information to help us:

(i) Predict what will happen in the future (see Figure 2), and

fig2

(ii) Shape what happens in the future (see Figure 3).

fig3

 

Data-Generating System

fig4

 

We use data from past events to predict and shape what happens in the future. Our predictions will only be valid if we correctly and accurately interpret the data we use as the basis for our predictions. To interpret the data correctly, and thus use it to make valid predictions, we must understand the data-generating system from which our data were collected. We must understand first, the motivations of the actors and the context in which the actions took place. These are shaped by both intrinsic factors (Customer/Actor) and extrinsic factors (Context/Environment, Content Provider). To make accurate predictions, we must also understand the context in which the data were collected (Context/Environment, Customer/Actor, Data Collector).

The data-generating system thus comprises four sets of influencers (see Figure 5): Content Provider, Customer/Actor, Data Collector, and Context/Environment.

fig5

  1. Content Provider: Provides content to Customers. Content Provider shapes the environment through his (a) selection of content provided to Customers, and (b) choice architecture used for presentation of content to Customers.
  2. Customer/Actor: Takes actions motivated by Intrinsic and extrinsic motivators, in both tangible and intangible form.
  3. Data Collector: Collects the information Analysts use about what happened. Data Collector shapes the information collected through his (a) choice of Customers from which to collect information, (b) design of data collection instruments through which Customers provide data, and (c) collation of data collected from Customers.
  4. External Environment: Background information that shapes actions, including, for example, culture, law, access to alternatives, degree of anonymity, and the state of the economy. The External Environment (a) shapes and primes Customers actions, (b) affects the information Customers provide, and/or (c) determines the generalizability of the information provided by and/or collected from Customers.
  5. Data Scientist/Analyst: Analyzes data collected by Data Collectors.

The data-generating system is a complex adaptive system. It changes and adapts to the evolving environment.

The Future Is Increasingly Difficult to Predict

When extrinsic factors (acts of nature, choice architecture, available alternatives) influence what happens (nature’s actions, human actions), analysts must understand (capture and measure) those extrinsic factors. Furthermore, to be able to shape outcomes, you must generally be able to control those extrinsic factors.

Our environments are becoming increasingly complex. At the same time, extrinsic factors are playing a larger role in shaping outcomes. This larger impact of extrinsic factors on peoples’ actions makes it difficult to use past information to predict, not to mention shape, future outcomes. This is true for two reasons. First, it’s difficult to capture the full environment – all the influencers – in which past actions took place. As a result, it is difficult to figure out precisely why events turned out the way they did. And second, future environments are unpredictable. Unpredictable environments that play a large role in creating outcomes makes it difficult to predict – not to mention shape – what those outcomes will be.

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