<p>For a PID system to work, you need to be (at least approximately) correct about the relationship between the variables you are trying to manage. Unfortunately the relationships between macroeconomic variables as predicted by mainstream economics has such a poor correspondence with reality, that any PID system based on these theories is doomed to fail.</p>

CC BY-SA 3.0
2016-10-17T08:51:35.267Z
2016-10-17T08:51:35.267Z
Mick
<p>A small addendum. Lars Peter Hansen and Thomas J. Sargent wrote the book "Robustness", which is an attempt to apply robust control to economics. They treated robust control from a game theoretical perspective.</p>

<p>In general, ecomics uses optimal control theory, which was the state of the art in the 1960s. The state of the art in the 1990s control theory (when I last worked in the field) was robust control, which fairly explicitly rejected the optimal control methodology.</p>

<p>Robust control introduced the notion of model error. (The game theoretic approach used by Hansen and Sargent attempts to emulate uncertainty via game theory, but the equivalence between model uncertainty and the game theoretic approach breaks down for nonlinear systems.) Without taking into account the reality that our models are incorrect, optimal control strategies tended to fail. A test pilot was killed by a defective optimal controller, and certification boards barred such controllers from aircraft. This killed optimal control as a strategy for control design.</p>

CC BY-SA 4.0
2020-11-27T12:55:42.967Z
Interesting insights! Can you provide any reference related to "... robust control, which fairly explicitly rejected the optimal control methodology"? 

And also for these two:"the equivalence between model uncertainty and the game theoretic approach breaks down for nonlinear systems" and "A test pilot was killed by a defective optimal controller, and certification boards barred such controllers from aircraft. This killed optimal control as a strategy for control design"? Many thanks! :)
Barzi2001
CC BY-SA 4.0
2020-11-27T14:18:06.310Z
1) You’d need to read a text on robust control. The reference I would use is “Robust and Optimal Control,” by Zhou, Doyle, and Glover, but it’s out of print, and I only have a pre-publication draft manuscript. 2) The amount of work nonlinear robust was limited, but it was my field. The game theoretic approach worked for linear systems because the disturbance is additive; nonlinear systems are not additive. 3) The test pilot story was well known by people in the area, but it was either in the 1960s/early 1970s, no idea how to track down an official history.
Brian Romanchuk
CC BY-SA 4.0
2020-11-27T14:19:29.727Z
As an addendum: I worked with people who were developing aircraft controls in the early 1990s, the certification requirements would have made the use of optimal control impossible.
Brian Romanchuk
CC BY-SA 3.0
2017-04-16T12:26:48.633Z
2017-04-16T12:26:48.633Z
Brian Romanchuk
<p>Economists use optimal control both in microeconomics and in macroeconomics.
Your question is about economic policy in particular, but policy decisions can be guided both by micro and macro models.</p>

<p><strong>Macroeconomics</strong></p>

<p>Central banks do use both simple feedback loops (think Taylor rule) and optimal control analysis to guide decisions. There is <a href="https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/optimal-control-monetary-policy-in-frbus-20141121.html#fn1" rel="nofollow noreferrer">this Federal Reserve note</a> from 2014 with introductory information and some references. It describes FRB/US model, which helps find the optimal path for the federal funds rate to minimize a given loss function.</p>

<p><strong>Microeconomics</strong></p>

<p>Microeconomic models can also guide policy decisions, for example, there is an active literature on oligopoly from optimal control theory perspective, which historically focused on advertising (e.g. <a href="https://www.jstor.org/stable/2030250?seq=1#page_scan_tab_contents" rel="nofollow noreferrer">this survey from 1977</a>). These can be used to predict behavior of agents on micro level and motivate policy decisions as well. </p>

<p>Microeconomics even contains a nice extension of optimal control to a setting with multiple decision-makers called <a href="https://en.wikipedia.org/wiki/Differential_game" rel="nofollow noreferrer">differential game theory</a>, which I will attempt to loosely describe as non-cooperative optimal control. </p>

<p>These models are further from policy making, but more of "exact science" type. Searching this stackexchange for "optimal control" brings up some relevant results too.</p>

<p><strong>Drawbacks</strong></p>

<p>Speaking of Taylor rule, John Taylor and John Williams in <a href="http://www.frbsf.org/economic-research/files/wp10-10bk.pdf" rel="nofollow noreferrer">this 2010 paper</a> argue against complex rules in favor of simpler ones. They mention the more obvious problems of measurement error and unobservables, which we have to make assumptions about: expectations, learning etc. However, they also state that simpler rules have advantage of working in a wider set of settings in practice:</p>

<blockquote>
  <p>"One potential shortcoming of the optimal control approach is that it
  ignores uncertainty about the specification of the model. Although in
  principle one can incorporate various types of uncertainty to the
  analysis of optimal policy, in practice computational feasibility
  limits what can be done. As a result, existing optimal control policy
  analysis is typically done using a single reference model, which is
  assumed to be true." (page 29)</p>
</blockquote>

<p>The paper has other references and history of the topic.</p>

<p>In the same note by Federal reserve there is also a warning:</p>

<blockquote>
  <p>"These models are by necessity an abstraction of a much more complex
  economic reality, and hence the actual strategies followed by the
  Federal Reserve and other central banks necessarily retain an
  important judgmental component." </p>
</blockquote>

CC BY-SA 3.0
2017-03-23T04:10:18.207Z
2017-03-23T04:10:18.207Z
kitsune
<p>Economists have been exploring control theory applications to macro economics for decades. For example, <a href="http://www.nber.org/chapters/c10438.pdf" rel="noreferrer">here is a 40 year-old research paper written in 1976 on the topic</a>.</p>

top of page 2 (also numbered 171)

<blockquote>
  <p>In the past decade, a number of engineers and economists have asked the question: "If modern control theory can improve the guidance of airplanes and spacecraft, can it also help in the control of inflation and unemployment?"</p>
</blockquote>

<p>Non-linear adaptive human behavior, uncertainty and governmental decentralization of control (i.e., democracy) are the main reasons I believe control theory has not become a more common tool for policy makers. Economics is simply not an exact science. Whereas engineering is more so. So control theory is more useful in the engineering discipline.</p>

CC BY-SA 3.0
2016-10-23T19:39:22.710Z
Makes sense, sounds like a good research subject though. Thanks!
Charles Clayton
CC BY-SA 3.0
2016-07-14T05:32:15.847Z
2017-03-20T07:51:28.547Z
2017-03-20T07:51:28.547Z
FreeMarketUnicorn
<p>Do central banks use some form of engineering-style PID control systems/feedback loops to implement monetary policy? </p>

<p>I'm an electrical engineering student taking microeconomics/macroeconomics and a lot of it, at least in terms of government policy to control inflation and interest rates, seems applicable to control systems engineering. However, I can't find a great deal of hard literature about it. </p>

<p>Just as a for instance, one could use PID control to set an output, say inflation rate, to follow a specific course or remain steady given certain inputs, say GDP or interest rates or productivity, and given external disturbances, say Brexit. And even if the controller can't prevent catastrophes altogether, it can dampen the response so that sudden and jerky crashes are smoothed out.</p>

<p>Why or why not does economic policy use established control theory? If it does, what applications can this be seen?</p>

<p>Thanks in advance!</p>

CC BY-SA 3.0
2016-10-17T02:05:45.693Z
Please explain your acronyms. What PID stands for?
Alecos Papadopoulos
CC BY-SA 3.0
2016-10-17T02:09:13.940Z
@AlecosPapadopoulos https://en.wikipedia.org/wiki/PID_controller
Charles Clayton
CC BY-SA 3.0
2016-10-18T08:26:17.510Z
Note that there's a "dynamic programming" tag in http://economics.stackexchange.com
Rodrigo de Azevedo
CC BY-SA 3.0
2017-03-20T00:38:02.717Z
what are the application of calculus in biological field?
Ajith kumar k
CC BY-SA 3.0
2017-03-20T14:17:17.627Z
Economists use optimal control as well ! :) especially all stuff in growth models ! ;)
optimal control
<mathematical-economics><monetary-policy><optimal-control>
Does control system engineering have a place in economics?
Charles Clayton
# Does control system engineering have a place in economics?

(Asked by: username_0 on 2016-07-13)

Do central banks use some form of engineering-style PID control systems/feedback loops to implement monetary policy?

I'm an electrical engineering student taking microeconomics/macroeconomics and a lot of it, at least in terms of government policy to control inflation and interest rates, seems applicable to control systems engineering. However, I can't find a great deal of hard literature about it.

Just as a for instance, one could use PID control to set an output, say inflation rate, to follow a specific course or remain steady given certain inputs, say GDP or interest rates or productivity, and given external disturbances, say Brexit. And even if the controller can't prevent catastrophes altogether, it can dampen the response so that sudden and jerky crashes are smoothed out.

Why or why not does economic policy use established control theory? If it does, what applications can this be seen?

Thanks in advance!

**Tags:** mathematical-economics, monetary-policy, optimal-control

> **Comments:**
> **username_1**: Please explain your acronyms. What PID stands for?  
> **username_0**: @AlecosPapadopoulos https://en.wikipedia.org/wiki/PID_controller  
> **username_2**: Note that there's a "dynamic programming" tag in http://economics.stackexchange.com  
> **username_3**: what are the application of calculus in biological field?  
> **username_4**: Economists use username_4 as well ! :) especially all stuff in growth models ! ;)  

---

## 4 answers

### Answer by username_9 on 2016-07-14. Score: 9 | Accepted answer

Economists have been exploring control theory applications to macro economics for decades. For example, [here is a 40 year-old research paper written in 1976 on the topic](http://www.nber.org/chapters/c10438.pdf).

top of page 2 (also numbered 171)
> In the past decade, a number of engineers and economists have asked the question: "If modern control theory can improve the guidance of airplanes and spacecraft, can it also help in the control of inflation and unemployment?"

Non-linear adaptive human behavior, uncertainty and governmental decentralization of control (i.e., democracy) are the main reasons I believe control theory has not become a more common tool for policy makers. Economics is simply not an exact science. Whereas engineering is more so. So control theory is more useful in the engineering discipline.

> **Comments:**
> **username_0**: Makes sense, sounds like a good research subject though. Thanks!  

---

### Answer by username_5 on 2016-10-17. Score: 3

For a PID system to work, you need to be (at least approximately) correct about the relationship between the variables you are trying to manage. Unfortunately the relationships between macroeconomic variables as predicted by mainstream economics has such a poor correspondence with reality, that any PID system based on these theories is doomed to fail.


---

### Answer by username_6 on 2017-04-16. Score: 3

A small addendum. Lars Peter Hansen and Thomas J. Sargent wrote the book "Robustness", which is an attempt to apply robust control to economics. They treated robust control from a game theoretical perspective.

In general, ecomics uses username_4 theory, which was the state of the art in the 1960s. The state of the art in the 1990s control theory (when I last worked in the field) was robust control, which fairly explicitly rejected the username_4 methodology.

Robust control introduced the notion of model error. (The game theoretic approach used by Hansen and Sargent attempts to emulate uncertainty via game theory, but the equivalence between model uncertainty and the game theoretic approach breaks down for nonlinear systems.) Without taking into account the reality that our models are incorrect, username_4 strategies tended to fail. A test pilot was killed by a defective username_4ler, and certification boards barred such controllers from aircraft. This killed username_4 as a strategy for control design.

> **Comments:**
> **username_7**: Interesting insights! Can you provide any reference related to "... robust control, which fairly explicitly rejected the username_4 methodology"?   And also for these two:"the equivalence between model uncertainty and the game theoretic approach breaks down for nonlinear systems" and "A test pilot was killed by a defective username_4ler, and certification boards barred such controllers from aircraft. This killed username_4 as a strategy for control design"? Many thanks! :)  
> **username_6**: 1) You’d need to read a text on robust control. The reference I would use is “Robust and Optimal Control,” by Zhou, Doyle, and Glover, but it’s out of print, and I only have a pre-publication draft manuscript. 2) The amount of work nonlinear robust was limited, but it was my field. The game theoretic approach worked for linear systems because the disturbance is additive; nonlinear systems are not additive. 3) The test pilot story was well known by people in the area, but it was either in the 1960s/early 1970s, no idea how to track down an official history.  
> **username_6**: As an addendum: I worked with people who were developing aircraft controls in the early 1990s, the certification requirements would have made the use of username_4 impossible.  

---

### Answer by username_8 on 2017-03-23. Score: 2

Economists use username_4 both in microeconomics and in macroeconomics.
Your question is about economic policy in particular, but policy decisions can be guided both by micro and macro models.

**Macroeconomics**

Central banks do use both simple feedback loops (think Taylor rule) and username_4 analysis to guide decisions. There is [this Federal Reserve note](https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/optimal-control-monetary-policy-in-frbus-20141121.html#fn1) from 2014 with introductory information and some references. It describes FRB/US model, which helps find the optimal path for the federal funds rate to minimize a given loss function.

**Microeconomics**

Microeconomic models can also guide policy decisions, for example, there is an active literature on oligopoly from username_4 theory perspective, which historically focused on advertising (e.g. [this survey from 1977](https://www.jstor.org/stable/2030250?seq=1#page_scan_tab_contents)). These can be used to predict behavior of agents on micro level and motivate policy decisions as well.

Microeconomics even contains a nice extension of username_4 to a setting with multiple decision-makers called [differential game theory](https://en.wikipedia.org/wiki/Differential_game), which I will attempt to loosely describe as non-cooperative username_4.

These models are further from policy making, but more of "exact science" type. Searching this stackexchange for "username_4" brings up some relevant results too.

**Drawbacks**

Speaking of Taylor rule, John Taylor and John Williams in [this 2010 paper](http://www.frbsf.org/economic-research/files/wp10-10bk.pdf) argue against complex rules in favor of simpler ones. They mention the more obvious problems of measurement error and unobservables, which we have to make assumptions about: expectations, learning etc. However, they also state that simpler rules have advantage of working in a wider set of settings in practice:

> "One potential shortcoming of the username_4 approach is that it
> ignores uncertainty about the specification of the model. Although in
> principle one can incorporate various types of uncertainty to the
> analysis of optimal policy, in practice computational feasibility
> limits what can be done. As a result, existing username_4 policy
> analysis is typically done using a single reference model, which is
> assumed to be true." (page 29)

The paper has other references and history of the topic.

In the same note by Federal reserve there is also a warning:

> "These models are by necessity an abstraction of a much more complex
> economic reality, and hence the actual strategies followed by the
> Federal Reserve and other central banks necessarily retain an
> important judgmental component."


---

