Reinventing Capitalism in the Age of Big Data: Summary

I’ve finished this book a few months ago, and realized it’ll be useful for me to recap the points I’ve learnt from the book.

This book talks about how data is the new currency in this era, and how the capitalism landscape will change. The book also proposes some steps to manage this change, and how to make it a fairer market for all

Chapter 1: Reinventing Capitalism


Key ideas:

  • Difference between conventional markets and data-rich markets is the role of information flowing through them, and how the information gets translated into decisions
  • In data-rich markets, we no longer have to condense all our preferences to a single dollar sum value, which can be an oversimplification of our preference
  • There are short-comings in data rich markets, which are over reliance on the data, and lack of diversity of the data and algorithms. This could lead to a data-monopoly, where the company with the most data dominates the industry

Chapter 2: Communicative Coordination


Key ideas:

  • Human successes so far is predicated mainly on our ability to communicate and coordinate with each other. Coordination relies on our ability to communicate.
  • Technology, more specifically volume of information, is increasing our ability to communicate with each other, hence increasing our coordination tremendously.
  • Markets and firms help humans coordinate more efficiently through economies of scale, where managing the pay and decisions of several workers is easier than managing each individual.
  • In the Market, decisions are decentralized, producing the “Invisible hand”. In the firm, decisions are centralized, and top-down.

Chapter 3: Markets and Money


Key Ideas:

  • How the structure of the market is linked to information, and how the information flows and are translated to decisions.
  • Information, and the cost of transmitting them, is integral to the success of a Market.
  • Markets are decentralized, and so is the flow of information. There is no single point of failure in the market.
  • Cascades of bad information in the market will lead to a market crash (housing market bubble, stock market crashes)
  • A huge volume of information however, will cause our cognitive abilities to fail. Hence, we use money as a denomination to be a summary of our preferences.
  • Money encapsulates our preferences and priorities into a single unit of information: Price
  • Priced-based markets have been dominating for many years, but it might not be the best to represent the market in the information age.

Chapter 4: Data-Rich Markets


Key Ideas:

  • The solution is to replace or complement price with rich and comprehensive streams of data. Rather than relying solely on price as an information, we have other sources of data to provide information as well.
  • The difference between conventional Markets and Data-Rich Markets is the volume and variety of data flowing through it.
  • Three technologies are required to reconfigure the market
    • Use a standard language when comparing our preferences
    • Better match preferences along multiple dimensions
    • Devise effective way to comprehensively capture all our preferences
  • Those 3 technologies translate rich data into effective transaction decisions
  • One of the challenges of a Data-Rich Market is getting the labels correct for each data
  • Transformation from a conventional Market to a Data-Rich Marker:
    • Improvements in data ontology to extract data from multiple streams
    • Advances in preference matching algorithms
    • Machine Learning to identify preferences from user activity

Chapter 5: Companies and Control


Key Ideas:

  • Firms aim to offer human coordination at a lower cost via economies of scale.
  • Information flow in firms were transform from accounting for the past, to a strategic tool for future planning. Scientific Management is the term for collection and processing multiple sources of information in the firm for planning.
  • Information and decision making in firms are centralized at the top, with a few instances of delegation.

Chapter 6: Firm Futures


Key Ideas:

  • Firms persist when they operate more efficiently at organizing human effort.
  • A focus on improving efficiency relies on two factors:
    • Are there inefficiencies to be eliminated?
    • What is the time period to implement the improvements? (Over a longer time, the gains get marginally smaller)
  • Firms can either Automate decision making, or rearrange their internal organization, which indirectly changes their decision-making structure.
  • Restructuring of the organization is a human construct, while automation of decisions removes the decision making process away from the manager to a machine learning system.
  • Restructuring still leaves humans in place, while automating decisions completely removes the need for humans.
  • In future, firms may end up as one of the two forms
    • A firm that owns most of the resources needed for operations and employs some humans, but is manged and run by machines
    • A firm that relies on market mechanisms, but sheds away most of their organizational functions

Chapter 7: Capital Decline


Key Ideas:

  • In Data-Rich Markets, consumers no longer use price as an indicator of information. Money thus loses one of it’s functions, though it won’t lose it’s usefulness entirely.
  • As market participants look for and consider richer data, fewer of them will rely on money, and wont be willing to pay as much for it’s informational function. This is detrimental to banks.
  • Money thus cannot convey multiple sources of information to the consumer, and the consumers must rely on other means to obtain the information
  • As the economy shifts to being Data-Rich, most information flows will not come from the banks. Banks will still handle transactions, but it will not be the source of information as it used to be.
  • The demise of money as the primary conveyor of information means there will be a decline in the role of Capital as well
  • Capital conveys information. It signals that a company has assets for disposal that it can exchange for other factors of production.
  • As markets embrace information, the two functions of capital – information and value – will be separated. It will only hold value, but not information.
  • As capital is abundant, and fewer companies are looking for capital, this means supply of capital outstrips demand, and thus the returns on investments for capital is falling.
  • Banks thus try to cut cost, and reinvent themselves as information intermediaries

Chapter 8: Feedback Effects


Key Ideas:

  • There are three distinct effects at play when markets become concentrated
    • Scale Effect – Economies of scale
    • Network Effect – Value of service increases when more people engage in it
    • Feedback Effect – Systems gain feedback to learn
  • Scale decreases cost, Network expands utility, and Feedback improves the product.
  • Volume of data greatly increases the Feedback effect, and companies that hold monopoly over data have an advantage over smaller companies with little and poor data.
  • Machine learning systems thus undermine competition, and data hoarding can be anti-competitive.
  • Open algorithms are not as helpful as open data to combat anti-competitive behavior.
  • Large companies wont lose anything when they share a small portion of their data. Their products will still improve, but you would help other smaller companies improve their products as well.
  • Relying solely on one decision making machine learning model is also fatal, as it is a bottleneck for mistakes. There should thus also have competition for decision making systems

Chapter 9: Unbundling Work


Key Ideas:

  • Data-Rich Markets and data-driven machine learning systems are changing the way humans work. There is a decrease in the percentage of people participating in the labor force.
  • Automation will slowly replace humans in the workforce. There are two ways to respond to this shift:
    • Distributive
    • Participatory
  • Distributive: As income shifts from labor to capital, the automation income can be taxed. Example, taxing machine and computers running the machine learning models – Robotax
  • Participatory: Retraining workers who have been made redundant.
  • Proposal of Universal Basic Income: Just enough money for basic necessities like food, shelter and education.
  • Not all automation replaces work, but some can help relief work and make it easier. – labor augmenting
  • Companies that hold dominance in the market, with strong feedback, and monopoly on data lead to significant market concentration: Google, Facebook, Amazon.
  • We need to define elements of work, and make them flexible enough to be recombined. UBI provides flexibility in the human choice when choosing work, because some tension in income requirements is alleviated.

Chapter 10: Human Choice


Key Ideas:

  • In a world of automation and machine performing decision making, what would humans do?
  • Task of humans, is not to be efficient, like robots, but to be creative and adventurous and feel alive, unlike robots
  • By freeing our minds very several mundane and routine decisions, we can focus on things that really matter

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