Relevance: Mains: G.S paper III: Economy
Context:
- Recently, two big impediments limited what research economists could learn about the world with the powerful methods that mathematicians and statisticians.
Data summary and pattern recognition:
- It was starting in the early 19th century, developed to recognize and interpret patterns in noisy data.
• Data sets were small and costly, and computers were slow and expensive.
• So it is natural that as gains in computing power have dramatically reduced these impediments, economists have rushed to use big data and artificial intelligence to help them spot patterns in all sorts of activities and outcomes.
• Data summary and pattern recognition are big parts of the physical sciences as well.
What is a game?
- The mathematician John von Neumann defined a game as;
• a list of players;
• a list of actions available to each player;
• a list of how pay-offs accruing to each player depend on the actions of all players; and
• a timing protocol that tells who chooses what when.
• This elegant definition includes what we mean by a “constitution” or an “economic system”: a social understanding about who chooses what when.
improved outcomes might produce different “games”
- We want to conduct experiments to study how a hypothetical change in the rules of the game or in a pattern of observed behaviour by some “players” (say, government regulators or a central bank) might affect patterns of behaviour by the remaining players.
• Thus, “structural model builders” in economics seek to infer from historical patterns of behaviour a set of invariant parameters for hypothetical (often historically unprecedented) situations in which a government or regulator follows a new set of rules.
• The government has strategies, and the people have counter-strategies, according to a Chinese proverb.
What are the Challenges ahead to build up structural models?
- “Structural models” seek such invariant parameters in order to help regulators and market designers understand and predict data patterns under historically unprecedented situations.
• The challenging task of building structural models will benefit from rapidly developing branches of artificial intelligence (AI) that don’t involve more than pattern recognition.
Case study:
- AlphaGo team of computer scientists that created the algorithm to play the Chinese game Go combined a suite of tools that had been developed by specialists in statistics, simulation, decision theory, and game theory communities.
• Many of the tools used in just the right proportions to make an outstanding artificial Go player are also economists’ bread-and-butter tools for building structural models to study macroeconomics and industrial organization.
Physics vs. Economics:
- The economics differs from physics in a crucial respect.
• Pierre-Simon Laplace regarded “the present state of the universe as the effect of its past and the cause of its future,” the reverse is true in economics: what we expect other people to do later causes what we do now.
• The “rational expectations”, reflects a sense in which “the future causes the present” in economic systems. Taking this into account is at the core of building “structural” economic models.
• There are similar trade-offs with unemployment and disability insurance—insuring people against bad luck may weaken their incentive to provide for themselves—and for official bailouts of governments and firms.
Conclusion:
- Like physicists, economists use models and data to learn.
• They don’t learn new things until we appreciate that our old models cannot explain new data.
• They construct new models in light of how their predecessors failed.
• This explains how we have learned from past depressions and financial crises.
• Big data, faster computers and better algorithms, we might see patterns where once we heard only noise.