# Price equation

"Price's theorem" redirects here. For the theorem in general relativity, see Richard H. Price.

In the theory of evolution and natural selection, the Price equation (also known as Price's equation or Price's theorem) describes how a trait or gene changes in frequency over time. The equation uses a covariance between a trait and fitness to give a mathematical description of evolution and natural selection. It provides a way to understand the effects that gene transmission and natural selection have on the proportion of genes within each new generation of a population. The Price equation was derived by George R. Price, working in London to re-derive W.D. Hamilton's work on kin selection. The Price equation also has applications in economics. Examples of the Price equation can be found here: Price equation examples.[1]

## Statement

Example of Price equation for a trait under positive selection

The Price equation shows that a change in the average amount z of a trait in a population from one generation to the next (Δz) is determined by the covariance between the amounts zi of the trait for subpopulations i and the fitnesses wi of the subpopulations, together with the expected change in the amount of the trait value due to fitness, namely E(wi Δzi):

${\displaystyle \Delta {z}={\frac {1}{w}}\operatorname {cov} (w_{i},z_{i})+{\frac {1}{w}}\operatorname {E} (w_{i}\,\Delta z_{i}).}$

Here w is the average fitness over the population, and E and cov represent the population mean and covariance respectively.

The covariance term captures the effects of natural selection; if the covariance between fitness (wi) and the trait value (zi) is positive then the trait value is predicted to increase on average over population i. If the covariance is negative then the trait is deleterious and is predicted to decrease in frequency.

The second term (E(wi Δzi)) represents factors other than direct selection that can affect trait evolution. This term can encompass genetic drift, mutation bias, or meiotic drive. Additionally, this term can encompass the effects of multi-level selection or group selection.

Price (1972) referred to this as the "environment change" term, and denoted both terms using partial derivative notation (∂NS and ∂EC). This concept of environment includes interspecies and ecological effects. Price describes this as follows:

Fisher adopted the somewhat unusual point of view of regarding dominance and epistasis as being environment effects. For example, he writes (1941): ‘A change in the proportion of any pair of genes itself constitutes a change in the environment in which individuals of the species find themselves.’ Hence he regarded the natural selection effect on M as being limited to the additive or linear effects of changes in gene frequencies, while everything else - dominance, epistasis, population pressure, climate, and interactions with other species - he regarded as a matter of the environment.

— Price 1972, Fisher's 'fundamental theorem' made clear.

## Price equation and group selection

The Price equation given above examines how the trait value of individualsclarification needed (zi), denoted by the subscript i, relates to the fitness of that individual (wi).The first step in adapting the Price equation to group selection is to change the subscript on each term to reflect the average trait value (zg), and corresponding mean fitness (wg) of each groups (g), as opposed to each individual:

${\displaystyle \Delta {z}={\frac {1}{w}}\operatorname {cov} (w_{g},z_{g})+{\frac {1}{w}}\operatorname {E} (w_{g}\,\Delta z_{g})}$

Giving the estimated change in a trait between groups. To incorporate changes within groups, set Δzg to individual form of the Price equation giving:

{\displaystyle {\begin{aligned}\Delta {z}&={\frac {1}{w}}\operatorname {cov} (w_{g},z_{g})+{\frac {1}{w}}\operatorname {E} (w_{g}(\,{\frac {1}{w_{g}}}\operatorname {cov} (w_{g,i},z_{g,i})+{\frac {1}{w_{g}}}\operatorname {E} (w_{g,i}\Delta z_{g,i})))\\&={\frac {1}{w}}\operatorname {cov} (w_{g},z_{g})+{\frac {1}{w}}\operatorname {E} (\operatorname {cov} (w_{g,i},z_{g,i})+\operatorname {E} (w_{g,i}\Delta z_{g,i}))\\\end{aligned}}}

Using this formulation, the Price equation can yield insights into the evolution of altruistic traits. By definition, altruistic traits increase group fitness while decreasing individual fitness. Therefore, the covariance between the group average of an altruistic trait (zg) and group average fitness (wg) is positive (cov(wg, zg)>0), and the covariance between the individual investment in an altruistic trait and that individuals fitness is negative (cov(wi, zi)<0). Therefore, the Price equation shows that high between group variance (cov(wg, zg)) coupled with low within group variance (cov(wg,i, zg,i)) is required for altruistic traits to spread due to natural selection.

## Proof of the Price equation

Suppose there is a population of ${\displaystyle n}$ individuals over which the amount of a particular characteristic varies. Those ${\displaystyle n}$ individuals can be grouped by the amount of the characteristic that each displays. There can be as few as just one group of all ${\displaystyle n}$ individuals (consisting of a single shared value of the characteristic) and as many as ${\displaystyle n}$ groups of one individual each (consisting of ${\displaystyle n}$ distinct values of the characteristic). Index each group with ${\displaystyle i}$ so that the number of members in the group is ${\displaystyle n_{i}}$ and the value of the characteristic shared among all members of the group is ${\displaystyle z_{i}}$. Now assume that having ${\displaystyle z_{i}}$ of the characteristic is associated with having a fitness ${\displaystyle w_{i}}$ where the product ${\displaystyle w_{i}n_{i}}$ represents the number of offspring in the next generation. Denote this number of offspring from group ${\displaystyle i}$ by ${\displaystyle n_{i}'}$ so that ${\displaystyle w_{i}=n_{i}'/n_{i}}$. Let ${\displaystyle z_{i}'}$ be the average amount of the characteristic displayed by the offspring from group ${\displaystyle i}$. Denote the amount of change in characteristic in group ${\displaystyle i}$ by ${\displaystyle \Delta z_{i}}$ defined by

${\displaystyle \Delta {z_{i}}\;{\stackrel {\mathrm {def} }{=}}\;z_{i}'-z_{i}}$

Now take ${\displaystyle z}$ to be the average characteristic value in this population and ${\displaystyle z'}$ to be the average characteristic value in the next generation. Define the change in average characteristic by ${\displaystyle \Delta {z}}$. That is,

${\displaystyle \Delta {z}\;{\stackrel {\mathrm {def} }{=}}\;z'-z}$

Note that this is not the average value of ${\displaystyle \Delta {z_{i}}}$ (as it is possible that ${\displaystyle n_{i}\neq n'_{i}}$). Also take ${\displaystyle w}$ to be the average fitness of this population. The Price equation states:

${\displaystyle w\,\Delta {z}=\operatorname {cov} (w_{i},z_{i})+\operatorname {E} (w_{i}\,\Delta z_{i})}$

where the functions ${\displaystyle \operatorname {E} }$ and ${\displaystyle \operatorname {cov} }$ are respectively defined in Equations (1) and (2) below and are equivalent to the traditional definitions of sample mean and covariance; however, they are not meant to be statistical estimates of characteristics of a population. In particular, the Price equation is a deterministic difference equation that models the trajectory of the actual mean value of a characteristic along the flow of an actual population of individuals. Assuming that the mean fitness ${\displaystyle w}$ is not zero, it is often useful to write it as

${\displaystyle \Delta {z}={\frac {1}{w}}\operatorname {cov} (w_{i},z_{i})+{\frac {1}{w}}\operatorname {E} (w_{i}\,\Delta z_{i})}$

In the specific case that characteristic ${\displaystyle z_{i}=w_{i}}$ (i.e., fitness itself is the characteristic of interest), then Price's equation reformulates Fisher's fundamental theorem of natural selection.

To prove the Price equation, the following definitions are needed. If ${\displaystyle n_{i}}$ is the number of occurrences of a pair of real numbers ${\displaystyle x_{i}}$ and ${\displaystyle y_{i}}$, then:

• The mean of the ${\displaystyle x_{i}}$ values is:
 ${\displaystyle \operatorname {E} (x_{i})\;{\stackrel {\mathrm {def} }{=}}\;{\frac {1}{\sum _{i}n_{i}}}\sum _{i}x_{i}n_{i}}$ ${\displaystyle (1)}$
• The covariance between the ${\displaystyle x_{i}}$ and ${\displaystyle y_{i}}$ values is:
 ${\displaystyle \operatorname {cov} (x_{i},y_{i})\;{\stackrel {\mathrm {def} }{=}}\;{\frac {1}{\sum _{i}n_{i}}}\sum _{i}n_{i}[x_{i}-\operatorname {E} (x_{i})][y_{i}-\operatorname {E} (y_{i})]=\operatorname {E} (x_{i}y_{i})-\operatorname {E} (x_{i})\operatorname {E} (y_{i})}$ ${\displaystyle (2)}$

The notation ${\displaystyle \langle x_{i}\rangle =\operatorname {E} (x_{i})}$ will also be used when convenient.

Suppose there is a population of organisms all of which have a genetic characteristic described by some real number. For example, high values of the number represent an increased visual acuity over some other organism with a lower value of the characteristic. Groups can be defined in the population which are characterized by having the same value of the characteristic. Let subscript ${\displaystyle i}$ identify the group with characteristic ${\displaystyle z_{i}}$ and let ${\displaystyle n_{i}}$ be the number of organisms in that group. The total number of organisms is then ${\displaystyle n}$ where:

${\displaystyle n=\sum _{i}n_{i}}$

The average value of the characteristic ${\displaystyle z}$ is defined as:

 ${\displaystyle z\;{\stackrel {\mathrm {def} }{=}}\;\operatorname {E} (z_{i})={\frac {1}{n}}\sum _{i}z_{i}n_{i}}$ ${\displaystyle (3)}$

Now suppose that the population reproduces, all parents are eliminated, and then there is a selection process on the children, by which less fit children are removed from the reproducing population. After reproduction and selection, the population numbers for the child groups will change to ni. Primes will be used to denote child parameters, unprimed variables denote parent parameters.

The total number of children is n' where:

${\displaystyle n'=\sum _{i}n'_{i}}$

The fitness of group i will be defined to be the ratio of children to parents:

 ${\displaystyle w_{i}={\frac {n_{i}'}{n_{i}}}}$ ${\displaystyle (4)\,}$

with average fitness of the population being

 ${\displaystyle w\;{\stackrel {\mathrm {def} }{=}}\;\operatorname {E} (w_{i})={\frac {1}{n}}\sum _{i}w_{i}n_{i}={\frac {1}{n}}\sum _{i}{\frac {n_{i}'}{n_{i}}}n_{i}={\frac {1}{n}}\sum _{i}n_{i}'={\frac {n'}{n}}}$ ${\displaystyle (5)}$

The average value of the child characteristic will be z' where:

 ${\displaystyle z'={\frac {1}{n'}}\sum _{i}z'_{i}n_{i}'}$ ${\displaystyle (6)}$

where zi are the (possibly new) values of the characteristic in the child population. Equation (2) shows that:

 ${\displaystyle \operatorname {cov} (w_{i},z_{i})=\operatorname {E} (w_{i}z_{i})-wz}$ ${\displaystyle (7)}$

Call the change in characteristic value from parent to child populations ${\displaystyle \Delta z_{i}}$ so that ${\displaystyle \Delta z_{i}=z'_{i}-z_{i}}$. As seen in Equation (1), the expected value operator ${\displaystyle \operatorname {E} }$ is linear, so

 ${\displaystyle \operatorname {E} (w_{i}\,\Delta z_{i})=\operatorname {E} (w_{i}z'_{i})-\operatorname {E} (w_{i}z_{i})}$ ${\displaystyle (8)}$

Combining Equations (7) and (8) leads to

 ${\displaystyle \operatorname {cov} (w_{i},z_{i})+\operatorname {E} (w_{i}\,\Delta z_{i})=\left[\operatorname {E} (w_{i}z_{i})-wz\right]+\left[\operatorname {E} (w_{i}z'_{i})-\operatorname {E} (w_{i}z_{i})\right]=\operatorname {E} (w_{i}z'_{i})-wz}$ ${\displaystyle (9)}$

Now, let's compute the first term in the equality above. From Equation (1), we know that:

${\displaystyle \operatorname {E} (w_{i}z'_{i})={\frac {1}{n}}\sum _{i}w_{i}z'_{i}n_{i}}$

Substituting the definition of fitness, ${\textstyle w_{i}={\frac {n'_{i}}{n_{i}}}}$ (Equation (4)), we get:

 ${\displaystyle \operatorname {E} (w_{i}z'_{i})={\frac {1}{n}}\sum _{i}{\frac {n'_{i}}{n_{i}}}z'_{i}n_{i}={\frac {1}{n}}\sum _{i}n'_{i}z'_{i}={\frac {n'}{n}}~{\frac {1}{n'}}\sum _{i}z'_{i}n'_{i}}$ ${\displaystyle (10)}$

Next, substituting the definitions of average fitness (${\textstyle w={\frac {n'}{n}}}$) from Equation (5), and average child characteristics (${\textstyle z'}$) from Equation (6) gives the Price Equation:

${\displaystyle \operatorname {cov} (w_{i},z_{i})+\operatorname {E} (w_{i}\,\Delta z_{i})=wz'-wz=w\,\Delta z\,}$

## Simple Price equation

When the characteristic values zi do not change from the parent to the child generation, the second term in the Price equation becomes zero resulting in a simplified version of the Price equation:

${\displaystyle w\,\Delta z=\operatorname {cov} \left(w_{i},z_{i}\right)}$

which can be restated as:

${\displaystyle \Delta z=\operatorname {cov} \left(v_{i},z_{i}\right)}$

where vi is the fractional fitness: vi= wi/w.

This simple Price equation can be proven using the definition in Equation (2) above. It makes this fundamental statement about evolution: "If a certain inheritable characteristic is correlated with an increase in fractional fitness, the average value of that characteristic in the child population will be increased over that in the parent population."

### Applications of the Price equation

The Price equation can describe any system that changes over time but is most often applied in evolutionary biology. The evolution of sight provides an example of simple directional selection. The evolution of sickle cell anemia shows how a [heterozygote] advantage can affect trait evolution. The Price equation can also be applied to population context dependent traits such as the evolution of sex ratios. Additionally, the Price equation is flexible enough to model second order traits such as the evolution of mutability. The Price equation also provides an extension to Founder effect which shows change in population traits in different settlements

### Dynamical sufficiency and the simple Price equation

Sometimes the genetic model being used encodes enough information into the parameters used by the Price equation to allow the calculation of the parameters for all subsequent generations. This property is referred to as dynamical sufficiency. For simplicity, the following looks at dynamical sufficiency for the simple Price equation, but is also valid for the full Price equation.

Referring to the definition in Equation (2), the simple Price equation for the character z can be written:

${\displaystyle w(z'-z)=\langle w_{i}z_{i}\rangle -wz}$

For the second generation:

${\displaystyle w'(z''-z')=\langle w'_{i}z'_{i}\rangle -w'z'}$

The simple Price equation for z only gives us the value of z '  for the first generation, but does not give us the value of w '  and 〈w 'i z 'i 〉 which are needed to calculate z″ for the second generation. The variables w '  and 〈w 'i z 'i 〉 can both be thought of as characteristics of the first generation, so the Price equation can be used to calculate them as well:

{\displaystyle {\begin{aligned}w(w'-w)&=\langle w_{i}^{2}\rangle -w^{2}\\w\left(\langle w'_{i}z'_{i}\rangle -\langle w_{i}z_{i}\rangle \right)&=\langle w_{i}^{2}z_{i}\rangle -w\langle w_{i}z_{i}\rangle \end{aligned}}}

The five 0-generation variables w, z, 〈wi  zi 〉, 〈w2i 〉, and 〈w2i zi 〉 which must be known before proceeding to calculate the three first generation variables w ', z ', and 〈w 'i z 'i 〉, which are needed to calculate z″ for the second generation. It can be seen that in general the Price equation cannot be used to propagate forward in time unless there is a way of calculating the higher moments (〈wni 〉 and 〈wni zi 〉) from the lower moments in a way that is independent of the generation. Dynamical sufficiency means that such equations can be found in the genetic model, allowing the Price equation to be used alone as a propagator of the dynamics of the model forward in time.

## Full Price equation

The simple Price equation was based on the assumption that the characters zi do not change over one generation. If it is assumed that they do change, with zi being the value of the character in the child population, then the full Price equation must be used. A change in character can come about in a number of ways. The following two examples illustrate two such possibilities, each of which introduces new insight into the Price equation.

### Genotype fitness

We focus on the idea of the fitness of the genotype. The index i indicates the genotype and the number of type i genotypes in the child population is:

${\displaystyle n'_{i}=\sum _{j}w_{ji}n_{j}\,}$

which gives fitness:

${\displaystyle w_{i}={\frac {n'_{i}}{n_{i}}}}$

Since the individual mutability zi does not change, the average mutabilities will be:

{\displaystyle {\begin{aligned}z&={\frac {1}{n}}\sum _{i}z_{i}n_{i}\\z'&={\frac {1}{n'}}\sum _{i}z_{i}n'_{i}\end{aligned}}}

with these definitions, the simple Price equation now applies.

### Lineage fitness

In this case we want to look at the idea that fitness is measured by the number of children an organism has, regardless of their genotype. Note that we now have two methods of grouping, by lineage, and by genotype. It is this complication that will introduce the need for the full Price equation. The number of children an i-type organism has is:

${\displaystyle n'_{i}=n_{i}\sum _{j}w_{ij}\,}$

which gives fitness:

${\displaystyle w_{i}={\frac {n'_{i}}{n_{i}}}=\sum _{j}w_{ij}}$

We now have characters in the child population which are the average character of the i-th parent.

${\displaystyle z'_{j}={\frac {\sum _{i}n_{i}z_{i}w_{ij}}{\sum _{i}n_{i}w_{ij}}}}$

with global characters:

{\displaystyle {\begin{aligned}z&={\frac {1}{n}}\sum _{i}z_{i}n_{i}\\z'&={\frac {1}{n'}}\sum _{i}z_{i}n'_{i}\end{aligned}}}

with these definitions, the full Price equation now applies.

## Criticism of the use of the Price equation

The use of the change in average characteristic (z'-z) per generation as a measure of evolutionary progress is not always appropriate. There may be cases where the average remains unchanged (and the covariance between fitness and characteristic is zero) while evolution is nevertheless in progress.

A critical discussion of the use of the Price equation can be found in van Veelen (2005) "On the use of the Price equation" [2] and van Veelen et al. (2012) "Group selection and inclusive fitness are not equivalent; the Price equation vs. models and statistics".[3] A discussion of this criticism can be found in Frank (2012) [4]

## Cultural references

Price's equation features in the plot and title of the 2008 thriller film WΔZ.

The Price equation also features in posters in the computer game BioShock 2, in which a consumer of a "Brain Boost" tonic is seen deriving the Price equation while simultaneously reading a book. The game is set in the 1950s, substantially before Price's work.

## References

In-line references
1. ^ Knudsen, Thorbjørn (2004). "General selection theory and economic evolution: The Price equation and the replicator/interactor distinction". Journal of Economic Methodology. Taylor and Francis Journals. 11 (2): 147–173. doi:10.1080/13501780410001694109. Retrieved 2011-10-22.
2. ^ "On the use of the Price equation". J. Theor. Biol. 237: 412–26. December 2005. doi:10.1016/j.jtbi.2005.04.026. PMID 15953618.
3. ^ "Group selection and inclusive fitness are not equivalent; the Price equation vs. models and statistics". J. Theor. Biol. 299: 64–80. April 2012. doi:10.1016/j.jtbi.2011.07.025. PMID 21839750.
4. ^ Frank, S A (2012). "Natural Selection. IV. The Price Equation". Journal of Evolutionary biology: 1002–1019. arXiv:. doi:10.1111/j.1420-9101.2012.02498.
General references

• van Veelen, Matthijs; Julián García, Maurice W. Sabelis, and Martijn Egas (2010). "Call for a return to rigour in models (correspondence)". Nature. 467 (7316): 661. doi:10.1038/467661d. PMID 20930826.
• van Veelen, Matthijs; Julián García, Maurice W. Sabelis, and Martijn Egas (April 2012). "Group selection and inclusive fitness are not equivalent; the Price equation vs. models and statistics". Journal of Theoretical Biology. 299: 64–80. doi:10.1016/j.jtbi.2011.07.025. PMID 21839750.
• Day, T. (2006). "Insights from Price's equation into evolutionnary epidemiology". DIMACS Series in Discrete Mathematics and Theoretical Computer Science. 71: 23–43.