Wednesday, I will be giving a talk at the seminar at the European Network for Business and Industrial Statistics (ENBIS), to present our model on COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability. The slides can be downloaded from here.

# Tag Archives: control

# Modeling Dynamic Incentives: Application to Basketball

I will give a talk on “* Modeling Dynamic Incentives: Application to Basketball*” at the GERAD (Groupe d’études et de recherche en analyse des décisions) on

An important aspect of the strategy of most organizations is the provision of incentives to the employees to meet the organization’s objectives. Typically this implies tying pay to performance (see Prendergast, 1999). In order to reward employees for their effort, ﬁrms spend considerable resources on performance evaluations. In many cases, evaluation consists of comparing actual performance to a pre-deﬁned individual target. Another frequently used format is relative performance evaluation. Relative performance evaluation may motivate employees to work harder.But it may also be demoralizing and create an excessively competitive workplace, which may hinder overall performance; see Lazear (1989). Determining the overall impact of relative performance evaluation is crucial for companies. Economic research on relative performance evaluation has mainly focused on the comparison of ﬁnal performances between competitors,like in tournament theory, and on quantitative and subjective performance ratings (Lazear and Gibbs, 2009). In contrast, what happens during a competition and the impact of feedback frequency on effort have so far received little attention. Following Berger and Pope (2011), we decided to use a basketball application to get a better understanding of the role of the feedback information. Sports datasets allow to observe score and team behavior continuously (during a game but also during the season) which can be use as a proxy of the effort. Berger an Pope (2010) asked ”can loosing lead to winning ?” looking at the impact of the halftime score difference on winning probability in NCAA (college) and NBA (pro) games. More precisely, they studied whether a team loosing at halftime is more likely to win than expected using a logit model. They ﬁnd that usually the higher the score difference the more likely the are to win. But if the halftime score difference is around 0 they observe a discontinuity: loosing with a small difference (e.g. down by 1 point) can lead to increase the effort and win the game. In this paper we try answer the question ”when loosing lead to winning ?”.

# Optimal control, part 2

In the first part (here), we introduced Bellman’s idea of backward induction. But what if we consider now infinite time horizon ? Actually, the maths will be even more simple… and we will be able to use fixed pointed theorem to derive solutions.

**The mathematical framework**

Here, consider the following value function

and define

A sequence is said to be an admissible solution for starting point x,

if and . If we reformulate the dynamic programming idea, we obtain that if is a solution to problem , then for all , sequence is a solution to problem . It comes that function v is a solution of Bellman’s equation

Note that is can be ssen as some fixed point resul, since

i.e.

So far, it shouldn’t be so hard….

**Frank Ramsey’s model (discrete version)**

In 1928, Frank Ramsey wanted to understand the amount of savings in a dynamic perspective (in *how much of its income should a nation save*). Consider the following infinite horizon problem, where some planifier wants to maximize

subject to constraints , and .

Before looking at dynamic programming answers, we might start with standard Lagrangian optimization techniques.

Assuming concavity of utility function and production function, we should look only for interior solutions. Define the Lagrangian as

Thus, the first order conditions are then given by

and

Assume further some terminal condition, e.g.

(also called transversality condition). If we combine those two conditions, and assume that the first constraint is saturated, we obtain the so-called Euler equation,

It is also possible to use Bellman’s equation: given the dynamic of the capital

The first order condition states

But since v is unknown, so is its derivative. But from the enveloppe theroem, we obtain something like

where

We can then write

i.e.

and finally

which is, Euler’s equation.

- A specified model, with calculations

As in the previous post (here), consider a log utility function, and a power production function, and . The dynamic is then

Note that fixed points are here

and

Recall that the value function is defined as

A natural idea to derive the value function can be to iterate, i.e.

starting with a simple function, e.g the null function, at step 0. At step n=1

thus

At step n=2,

i.e.

The first order condition is then

and thus, we obtain

that can be plugged in the previous equation, i.e.

At step 3, we start from that new expression, derive the first order condition, and we get

and

and so on…

And finally, we can prove that

i.e. . Assuming that

actually, we can prove that

(and has a form that can be explicited).

# Optimal control, part 1

Since I had recently a request (from Benoît) about optimal control, I will start here a series of posts on that topic. But first, let us start with a simple problem, with discrete time, no randomness, and with a finite horizon. This might be a too simple framework to model complex problem, but that should be interesting to derive heuristic intuitions (I will skip here the mathematical problems, that can be found in very good books… references will come soon).

**An introduction to backward induction**

Before starting seriously, let us consider the following example: we want to reach the red city on the right from the red city on the left, as fast as possible. There are some roads, and the number is the number of hours it takes. Let us *prove *that the optimal way is the red one,

The first idea can be to calculate *all *possible trajectories, but with a large number of roads, the number of possible ways can soon be extremely large. An alternative can be to look backwards (like any students facing a question where the answer is given: start from the end, and try to find a possible way to reach it).

Numbers are greens are the number of hours that we still need one we’ve reached that point. Let us move again one step backward, and consider the orange points,

In the middle, we had to chose either to go up (it will still take 9 hours) or go down (and then 14 hours are necessary). Thus, the *optimal* strategy, once we’ve reach that point, it to take the 9 hour road. This will be idea the idea proposed by Bellman. Let us go backward again, to the purple cities. Again, we have to chose the shorter way,

From the top, the fastest road will take 13 hours, and from the bottom, it will take 16 hours. Thus, since it takes 10 hours to reach the top city, this road is necessarily faster than taking the one below (since it takes 8 hours to reach the nearest city).

Any this is it. We now have an intuitive idea of what should be done to find optimal strategies.

**The optimization problem, discrete with finite horizon**

Let us consider the following optimization problem: we want to find the optimal strategy that maximizes the following function

with simple constraints, such as (a state space) and (i.e. some dynamic constraints). Assume further that the starting point is given, i.e.

. In economic application, note that frequently i.e. we consider a discounted version of the value.

**The idea of dynamic programming**

The intuitive idea of dynamic programming is that if the optimal path from A to C goes throught B, then the path is optimal from B to C. Thus, it will be natural to consider backward induction techniques.

Thus, define

…

Then Bellman’s principle can be used to link those problems: if is a solution of the problem then, for all

, is a solution of problem .

Note that, so far, we assume that such an optimal sequence does exist. Thus, we get that for all x

and more generally,

He

nce, from a practical point of view, we solve those equation using a backward approch. I.e. first,

and then

and so on

… etc. It can be proved that the sequence is solution of if and only if for all ,

is solution of

So far, it does not look so difficult….

**A simple example**

Let denote the consumption at period *t*, and assume consumption yields utility

as long as the consumer lives. Assume the consumer is impatient, or has a stronger preference for present, so that he discounts future utility by a factor . Let be capital he got at time t. Assume that his initial capital is a given amount , and suppose that this period’s capital and consumption determine next period’s capital as

where is a positive constant and . Assume further that capital cannot be negative. Then the consumer’s problem is simply

given . Bellman’s equation is then

whih leads us to a simplier problem than the initial one, since only two variables are involved here and . And to solve that problem, we use backwards induction techniques.

Since is known, we can derive easily , and so on until . More precisely, given , we can get which is the maximum of function

with . One can see that the following function is a possible solution

where each is a constant. Further, the optimal amount to consume at time is

i.e., if we explicit those expressions

…etc.

**A reformulation of the optimisation problem**

Another way of expressing the optimization problem is the following: was the variable of interest, was the control variable, and . Thus, the programm

can be expressed as

Several extension can be considered,

- consider an infinite sum

- consider a random component

- consider a continuous version

But those items will be for posts that I still have to write down….