04/09/2015

Introduction

What am I doing

  • Less than fully rational agents are hard

  • Start from the opposite direction

  • Use simple trial and error

Who cares?

  • Modeling Contribution

  • "Suppose first that the market price is above the equilibrium price […] There is a surplus of the good: suppliers are unable to sell all they want at the going price. […] They respond to the surplus by cutting their prices. Prices continue to fall until the market reaches the equilibrium."

  • Robustness Check

The Thermostat

Feedback

The Agent problem

The agent diagram

Enters the PID Controller

Feedback

Formally

  • A seller wants to sell \(y^*\) kilos of cheese each day. Today he charged price \(p_t\) and sold \(y_t\) kilos. How should he sets tomorrow's price \(p_{t+1}\)

  • \(e_t = y^* -y_t\)

  • \(p_{t+1} = a e_t + b \sum_0^t e_{\tau} d\tau + c \frac{de_t}{d_t} + p_0\)

Why PI(D) Controllers

  • Absence of model knowledge

  • Assume unsophisticated users

  • Simple to code

  • Simple to fit

Cyber-ECB

Alternatives

  • Reactive/Subsumption Architecture

  • Zero-Intelligence (Plus)

  • Probe and Adjust

  • Gintis

Zero Knowledge Microeconomics

Zero-Knowledge Seller Demo

Adapting over Learning - Change in Demand

Adapting over Learning - Change in Endowment

Adding production

  • Making 50 kilos of cheese a day require action over multiple markets: milk, workers, sales. How do we generalize PID pricing over multiple markets?

  • How do we decide how many kilos of cheese to produce in order to maximize profits?

Multiple Controls and Fixed Target

Marginal Maximizer

  • Profits are maximized when marginal benefits equal marginal costs

  • \(\text{Marginal Benefit: } p_t = w_t \text{ :Marginal Costs}\)

Simple Marginal Example

We know how to deal with sliders

  • Use a PID controller to find how for which production target \(p_t = w_t\)

  • Lower Frequency

Decisions are fed in

Information feeds back

Independent Control and Flexible Target

Competitive (5 firms), known price impacts

Market power

  • If you have market power, you need to take that into account

  • \(\frac{\partial \Pi}{\partial q} = 0\)

  • \(p + q \frac{\partial p}{\partial q} = w + q \frac{\partial w}{\partial q}\)

  • \(p_t + \mu_p = w_t + \mu_w\)

  • Use a PID with error \(p_t + \mu_p - w_t - \mu_w\)

Monopolist, known price impacts

Learning price impacts

  • PI trial and error produce paired data \(p_t,y_t\)

  • Can run a linear regression over them to discover \(\mu_p\)

  • Use Kalman Filters

Learning works

Feedback

Feedforward

Ramsey Cass Koopmans

Real Business Cycle

IS-LM models

Cybernetics

  • Gunnery Control and Norbert Wiener

  • "The world, understood cybernetically, was a world of goal-oriented feedback mechanisms with learning. Cybernetics,then, took computer-controlled gun control and layered it in an ontologically indiscriminate fashion across the academic disciplinary board."

Whatever happened to…

Zero Knowledge Supply Chains

Sticky Prices

  • Two Large Literatures:
    • Price Stickiness
    • Bullwhip effects
  • Naive trial and error stops working

  • A model where sticky prices achieve equilibrium and flexible prices don't

If you ever get lost

Undelayed

10 Days Delay

20 Days Delay

Stickiness Deals with Delays (1)

Stickiness Deals with Delays (2)

Supply-Chain

Supply-Chains as a Source of Delays

Delays break supply-chains

## NULL

Stickiness restores equilibrium

## NULL

Sticky supply chains get to equilibrium's price

Sticky supply chains get to equilibrium's quantity

Learning degrades with sticky delays and supply-chains

Zero Knowledge Macroeconomics

Source

  • Leijonhufvud "Keynes and the Keynesians"

Marshallian

Keynesian

Marshallian Micro

Keynesian Micro

Same in Micro

Macroeconomics

  • I mostly stay clear from macro ABM
  • Demand equals wages paid
  • No savings

Marshallian Macro

Keynesian Macro

Demand Shock

Keynesian is faster

Keynesian is worse

Labor flexibility as speed

Labor flexibility as productivity

Conclusion

Conclusion

  • Economic Feedback Spectrum
  • Tuning
  • Thank you!