Maximum sustainable yield

From Wikipedia, the free encyclopedia

 

In population ecology and economics, maximum sustainable yield or MSY is theoretically, the largest yield (or catch) that can be taken from a species' stock over an indefinite period. Fundamental to the notion of sustainable harvest, the concept of MSY aims to maintain the population size at the point of maximum growth rate by harvesting the individuals that would normally be added to the population, allowing the population to continue to be productive indefinitely. Under the assumption of logistic growth, resource limitation does not constrain individuals' reproductive rates when populations are small, but because there are few individuals, the overall yield is small. At intermediate population densities, also represented by half the carrying capacity, individuals are able to breed to their maximum rate. At this point, called the maximum sustainable yield, there is a surplus of individuals that can be harvested because growth of the population is at its maximum point due to the large number of reproducing individuals. Above this point, density dependent factors increasingly limit breeding until the population reaches carrying capacity. At this point, there are no surplus individuals to be harvested and yield drops to zero. The maximum sustainable yield is usually higher than the optimum sustainable yield and maximum economic yield.

MSY is extensively used for fisheries management. Unlike the logistic (Schaefer) model,^[1] MSY has been refined in most modern fisheries models and occurs at around 30% ^[2] of the unexploited population size. This fraction differs among populations depending on the life history of the species and the age-specific selectivity of the fishing method.

However, the approach has been widely criticized as ignoring several key factors involved in fisheries management and has led to the devastating collapse of many fisheries. As a simple calculation, it ignores the size and age of the animal being taken, its reproductive status, and it focuses solely on the species in question, ignoring the damage to the ecosystem caused by the designated level of exploitation and the issue of bycatch. Among conservation biologists it is widely regarded as dangerous and misused.^[3][4]

History

The concept of MSY as a fisheries management strategy developed in Belmar, New Jersey, in the early 1930s.^[5][6][7] It increased in popularity in the 1950s with the advent of surplus-production models with explicitly estimate MSY.^[1] As an apparently simple and logical management goal, combined with the lack of other simple management goals of the time, MSY was adopted as the primary management goal by several international organizations (e.g., IWC, IATTC,^[8] ICCAT, ICNAF), and individual countries.^[9]

Between 1949 and 1955, the U.S. maneuvered to have MSY declared the goal of international fisheries management (Johnson 2007). The international MSY treaty that was eventually adopted in 1955 gave foreign fleets the right to fish off any coast. Nations that wanted to exclude foreign boats had to first prove that its fish were overfished.^[10]

As experience was gained with the model, it became apparent to some researchers that it lacked the capability to deal with the real world operational complexities and the influence of trophic and other interactions. In 1977, Peter Larkin wrote its epitaph, challenging the goal of maximum sustained yield on several grounds: It put populations at too much risk; it did not account for spatial variability in productivity; it did not account for species other than the focus of the fishery; it considered only the benefits, not the costs, of fishing; and it was sensitive to political pressure.^[11] In fact, none of these criticisms was aimed at sustainability as a goal. The first one noted that seeking the absolute MSY with uncertain parameters was risky. The rest point out that the goal of MSY was not holistic; it left out too many relevant features.^[10]

Some managers began to use more conservative quota recommendations, but the influence of the MSY model for fisheries management still prevailed. Even while the scientific community was beginning to question the appropriateness and effectiveness of MSY as a management goal,^[11][12] it was incorporated into the 1982 United Nations Convention for the Law of the Sea, thus ensuring its integration into national and international fisheries acts and laws.^[9] According to Walters and Maguire, an ‘‘institutional juggernaut had been set in motion’’, climaxing in the early 1990s with the collapse of northern cod.^[13]

Modelling MSY

Population growth

The key assumption behind all sustainable harvesting models such as MSY is that populations of organisms grow and replace themselves – that is, they are renewable resources. Additionally it is assumed that because the growth rates, survival rates, and reproductive rates increase when harvesting reduces population density,^[5] they produce a surplus of biomass that can be harvested. Otherwise, sustainable harvest would not be possible.
Another assumption of renewable resource harvesting is that populations of organisms do not continue to grow indefinitely; they reach an equilibrium population size, which occurs when the number of individuals matches the resources available to the population (i.e., assume classic logistic growth). At this equilibrium population size, called the carrying capacity, the population remains at a stable size.^[14]

Figure 1

The logistic model (or logistic function) is a function that is used to describe bounded population growth under the previous two assumptions. The logistic function is bounded at both extremes: when there are not individuals to reproduce, and when there is an equilibrium number of individuals (i.e., at carrying capacity). Under the logistic model, population growth rate between these two limits is most often assumed to be sigmoidal (Figure 1). There is scientific evidence that some populations do grow in a logistic fashion towards a stable equilibrium – a commonly cited example is the logistic growth of yeast.

The equation describing logistic growth is:^[14]

${\displaystyle N_{t}={\frac {K}{1+{\frac {K-N_{0}}{N_{0}}}e^{-rt}}}}$ (equation 1.1)

The parameter values are:

${\displaystyle N_{t}}$=The population size at time t

${\displaystyle K}$=The carrying capacity of the population

${\displaystyle N_{0}}$= The population size at time zero

${\displaystyle r}$= the intrinsic rate of population increase (the rate at which the population grows when it is very small)

From the logistic function, the population size at any point can be calculated as long as ${\displaystyle r}$, ${\displaystyle K}$, and ${\displaystyle N_{0}}$ are known.

Figure 2

Differentiating equation 1.1 give an expression for how the rate of population increases as t increases. At first, the population growth rate is fast, but it begins to slow as times goes on until it levels off to the maximum growth rate, after which it begins to decrease (figure 2).

The equation for figure 2 is the differential of equation 1.1 (Verhulst's 1838 growth model):^[14]

${\displaystyle {\frac {dN}{dt}}=rN\left(1-{\frac {N}{K}}\right)}$ (equation 1.2)

${\displaystyle {\frac {dN}{dt}}}$ can be understood as the change in population (N) with respect to a change in time (t). Equation 1.2 is the usual way in which logistic growth is represented mathematically and has several important features. First, at very low population sizes, the value of ${\displaystyle {\frac {N}{K}}}$ is small, so the population growth rate is approximately equal to ${\displaystyle rN}$, meaning the population is growing exponentially at a rate r (the intrinsic rate of population increase). Despite this, the population growth rate is very low (low values on the y-axis of figure 2) because, even though each individual is reproducing at a high rate, there are few reproducing individuals present. Conversely, when the population is large the value of ${\displaystyle {\frac {N}{K}}}$ approaches 1 effectively reducing the terms inside the brackets of equation 1.2 to zero. The effect is that the population growth rate is again very low, because either each individual is hardly reproducing or mortality rates are high.^[14] As a result of these two extremes, the population growth rate is maximum at an intermediate population or half the carrying capacity (${\displaystyle N={\frac {K}{2}}}$).

MSY model

Figure 3

The simplest way to model harvesting is to modify the logistic equation so that a certain number of individuals is continuously removed:^[14]

${\displaystyle {\frac {dN}{dt}}=rN\left(1-{\frac {N}{K}}\right)-H}$ (equation 1.3)

Where H represents the number of individuals being removed from the population – that is, the harvesting rate. When H is constant, the population will be at equilibrium when the number of individuals being removed is equal to the population growth rate (figure 3). The equilibrium population size under a particular harvesting regime can be found when the population is not growing – that is, when ${\displaystyle {\frac {dN}{dt}}=0}$. This occurs when the population growth rate is the same as the harvest rate:

${\displaystyle rN\left(1-{\frac {N}{K}}\right)=H}$

Figure 3 shows how growth rate varies with population density. For low densities (far from carrying capacity), there is little addition (or "recruitment") to the population, simply because there are few organisms to give birth. At high densities, though, there is intense competition for resources, and growth rate is again low because the death rate is high. In between these two extremes, the population growth rate rises to a maximum value (${\displaystyle N_{MSY}}$). This maximum point represents the maximum number of individuals that can be added to a population by natural processes. If more individuals than this are removed from the population, the population is at risk for decline to extinction.^[15] The maximum number that can be harvested in a sustainable manner, called the maximum sustainable yield, is given by this maximum point.

Figure 3 also shows several possible values for the harvesting rate, H. At ${\displaystyle H_{1}}$, there are two possible population equilibrium points: a low population size (${\displaystyle N_{a}}$) and a high one (${\displaystyle N_{b}}$). At ${\displaystyle H_{2}}$, a slightly higher harvest rate, however there is only one equilibrium point (at ${\displaystyle N_{MSY}}$), which is the population size that produces the maximum growth rate. With logistic growth, this point, called the maximum sustainable yield, is where the population size is half the carrying capacity (or ${\displaystyle N={\frac {K}{2}}}$). The maximum sustainable yield is the largest yield that can be taken from a population at equilibrium. In figure 3, if ${\displaystyle H}$ is higher than ${\displaystyle H_{2}}$, the harvesting would exceed the population's capacity to replace itself at any population size (${\displaystyle H_{3}}$ in figure 3). Because harvesting rate is higher than the population growth rate at all values of ${\displaystyle N}$, this rate of harvesting is not sustainable.

An important feature of the MSY model is how harvested populations respond to environmental fluctuations or illegal offtake. Consider a population at ${\displaystyle N_{b}}$ harvested at a constant harvest level ${\displaystyle H_{1}}$. If the population falls (due to a bad winter or illegal harvest) this will ease density-dependent population regulation and increase yield, moving the population back to ${\displaystyle N_{b}}$, a stable equilibrium. In this case, a negative feedback loop creates stability. The lower equilibrium point for the constant harvest level ${\displaystyle H_{1}}$ is not stable however; a population crash or illegal harvesting will decrease population yield farther below the current harvest level, creating a positive feedback loop leading to extinction. Harvesting at ${\displaystyle N_{MSY}}$ is also potentially unstable. A small decrease in the population can lead to a positive feedback loop and extinction if the harvesting regime (${\displaystyle H_{2}}$) is not reduced. Thus, some consider harvesting at MSY to be unsafe on ecological and economic grounds. The MSY model itself can be modified to harvest a certain percentage of the population or with constant effort constraints rather than an actual number, thereby avoiding some of its instabilities.^[15]

The MSY equilibrium point is semi-stable – a small increase in population size is compensated for, a small decrease to extinction if H is not decreased. Harvesting at MSY is therefore dangerous because it is on a knife-edge – any small population decline leads to a positive feedback, with the population declining rapidly to extinction if the number of harvested stays the same.

The formula for maximum sustained harvest (${\displaystyle H}$) is one-fourth the maximum population or carrying capacity (${\displaystyle K}$) times the intrinsic rate of growth (${\displaystyle r}$).^[17]

${\displaystyle H={\frac {Kr}{4}}}$

For demographically structured populations

The principle of MSY often holds for age-structured populations as well.^[18] The calculations can be more complicated, and the results often depend on whether density dependence occurs in the larval stage (often modeled as density dependent reproduction) and/or other life stages.^[19] It has been shown that if density dependence only acts on larva, then there is an optimal life stage (size or age class) to harvest, with no harvest of all other life stages.^[18] Hence the optimal strategy is to harvest this most valuable life-stage at MSY.^[20] However, in age and stage-structured models, a constant MSY does not always exist. In such cases, cyclic harvest is optimal where the yield and resource fluctuate in size, through time.^[21] In addition, environmental stochasticity interacts with demographically structured populations in fundamentally different ways than for unstructured populations when determining optimal harvest. In fact, the optimal biomass to be left in the ocean, when fished at MSY, can be either higher or lower than in analogous deterministic models, depending on the details of the density dependent recruitment function, if stage-structure is also included in the model.^[22]

Implications of MSY model

Starting to harvest a previously unharvested population will always lead to a decrease in the population size. That is, it is impossible for a harvested population to remain at its original carrying capacity. Instead, the population will either stabilize at a new lower equilibrium size or, if the harvesting rate is too high, decline to zero.

The reason why populations can be sustainably harvested is that they exhibit a density-dependent response.^[15][16] This means that at any population size below K, the population is producing a surplus yield that is available for harvesting without reducing population size. Density dependence is the regulator process that allows the population to return to equilibrium after a perturbation. The logistic equation assumes that density dependence takes the form of negative feedback.^[16]

If a constant number of individuals is harvested from a population at a level greater than the MSY, the population will decline to extinction. Harvesting below the MSY level leads to a stable equilibrium population if the starting population is above the unstable equilibrium population size.

Uses of MSY

MSY has been especially influential in the management of renewable biological resources such as commercially important fish and wildlife. In fisheries terms, maximum sustainable yield (MSY) is the largest average catch that can be captured from a stock under existing environmental conditions.^[23] MSY aims at a balance between too much and too little harvest to keep the population at some intermediate abundance with a maximum replacement rate.

Relating to MSY, the maximum economic yield (MEY) is the level of catch that provides the maximum net economic benefits or profits to society.^[24][25] Like optimum sustainable yield, MEY is usually less than MSY.

Limitations of MSY approach

Although it is widely practiced by state and federal government agencies regulating wildlife, forests, and fishing, MSY has come under heavy criticism by ecologists and others from both theoretical and practical reasons.^[16] The concept of maximum sustainable yield is not always easy to apply in practice. Estimation problems arise due to poor assumptions in some models and lack of reliability of the data.^[9][26] Biologists, for example, do not always have enough data to make a clear determination of the population's size and growth rate. Calculating the point at which a population begins to slow from competition is also very difficult. The concept of MSY also tends to treat all individuals in the population as identical, thereby ignoring all aspects of population structure such as size or age classes and their differential rates of growth, survival, and reproduction.^[26]

As a management goal, the static interpretation of MSY (i.e., MSY as a fixed catch that can be taken year after year) is generally not appropriate because it ignores the fact that fish populations undergo natural fluctuations (i.e., MSY treats the environment as unvarying) in abundance and will usually ultimately become severely depleted under a constant-catch strategy.^[26] Thus, most fisheries scientists now interpret MSY in a more dynamic sense as the maximum average yield (MAY) obtained by applying a specific harvesting strategy to a fluctuating resource.^[9] Or as an optimal "escapement strategy", where escapement means the amount of fish that must remain in the ocean [rather than the amount of fish that can be harvested]. An escapement strategy is often the optimal strategy for maximizing expected yield of a harvested, stochastically fluctuating population.^[27]

However, the limitations of MSY, does not mean it performs worse than humans using their best intuitive judgment. Experiments using students in natural resource management classes suggest that people using their past experience, intuition, and best judgement to manage a fishery generate far less long term yield compared to a computer using an MSY calculation, even when that calculation comes from incorrect population dynamic models.^[28]

For a more contemporary description of MSY and its calculation see ^[29]

Orange roughy

An example of errors in estimating the population dynamics of a species occurred within the New Zealand Orange roughy fishery. Early quotas were based on an assumption that the orange roughy had a fairly short lifespan and bred relatively quickly. However, it was later discovered that the orange roughy lived a long time and had bred slowly (~30 years). By this stage stocks had been largely depleted.

Overfishing

All around the world, from the arctic to the tropics, there is a crisis in the world's fisheries.^[30] Until fairly recently it was assumed that our marine resources were limitless.
In recent years however, an accelerating decline has been observed in the productivity of many important fisheries.^[31] Fisheries which have been devastated in recent times include (but are not limited to) the great whale fisheries, the Grand Bank fisheries of the western Atlantic, and the Peruvian anchovy fishery.^[32] Recent assessments by the United Nations Food and Agriculture Organization (FAO) of the state of the world's fisheries indicate a levelling off of landings in the 1990s, at about 100 million tons.^[33]

In addition, the composition of global catches has changed.^[34] As fishers deplete larger, long-lived predatory fish species such as cod, tuna, shark, and snapper, they move down to the next level – to species that tend to be smaller, shorter-lived, and less valuable.^[35]

Overfishing is a classic example of the tragedy of the commons.^[32]

Optimum sustainable yield

In population ecology and economics, optimum sustainable yield is the level of effort (LOE) that maximizes the difference between total revenue and total cost. Or, where marginal revenue equals marginal cost. This level of effort maximizes the economic profit, or rent, of the resource being utilized. It usually corresponds to an effort level lower than that of maximum sustainable yield. In environmental science, optimum sustainable yield is the largest economical yield of a renewable resource achievable over a long time period without decreasing the ability of the population or its environment to support the continuation of this level of yield.

Population dynamics

From Wikipedia, the free encyclopedia

 

Map of population trends of native and invasive species of jellyfish^[1]

 

  Increase (high certainty)

  Increase (low certainty)

  Stable/variable

  Decrease

  No data

Population dynamics is the branch of life sciences that studies the size and age composition of populations as dynamical systems, and the biological and environmental processes driving them (such as birth and death rates, and by immigration and emigration). Example scenarios are ageing populations, population growth, or population decline.

History

Population dynamics has traditionally been the dominant branch of mathematical biology, which has a history of more than 210 years, although more recently the scope of mathematical biology has greatly expanded. The first principle of population dynamics is widely regarded as the exponential law of Malthus, as modeled by the Malthusian growth model. The early period was dominated by demographic studies such as the work of Benjamin Gompertz and Pierre François Verhulst in the early 19th century, who refined and adjusted the Malthusian demographic model.

A more general model formulation was proposed by F.J. Richards in 1959, further expanded by Simon Hopkins, in which the models of Gompertz, Verhulst and also Ludwig von Bertalanffy are covered as special cases of the general formulation. The Lotka–Volterra predator-prey equations are another famous example, as well as the alternative Arditi–Ginzburg equations. The computer game SimCity and the MMORPG Ultima Online, among others, tried to simulate some of these population dynamics.

In the past 30 years, population dynamics has been complemented by evolutionary game theory, developed first by John Maynard Smith. Under these dynamics, evolutionary biology concepts may take a deterministic mathematical form. Population dynamics overlap with another active area of research in mathematical biology: mathematical epidemiology, the study of infectious disease affecting populations. Various models of viral spread have been proposed and analyzed, and provide important results that may be applied to health policy decisions.

Intrinsic rate of increase

The rate at which a population increases in size if there are no density-dependent forces regulating the population is known as the intrinsic rate of increase. It is

${\displaystyle {\dfrac {dN}{dt}}{\dfrac {1}{N}}=r}$

where the derivative ${\displaystyle dN/dt}$ is the rate of increase of the population, N is the population size, and r is the intrinsic rate of increase. Thus r is the maximum theoretical rate of increase of a population per individual – that is, the maximum population growth rate. The concept is commonly used in insect population biology to determine how environmental factors affect the rate at which pest populations increase. See also exponential population growth and logistic population growth.^[2]

Common mathematical models

Exponential population growth

Exponential growth describes unregulated reproduction. It is very unusual to see this in nature. In the last 100 years, human population growth has appeared to be exponential. In the long run, however, it is not likely. Paul Ehrlich and Thomas Malthus believed that human population growth would lead to overpopulation and starvation due to scarcity of resources. They believed that human population would grow at rate in which they exceed the ability at which humans can find food. In the future, humans would be unable to feed large populations. The biological assumptions of exponential growth is that the per capita growth rate is constant. Growth is not limited by resource scarcity or predation.^[3]

Simple discrete time exponential model

${\displaystyle N_{t+1}=\lambda N_{t}}$

where λ is the discrete-time per capita growth rate. At λ = 1, we get a linear line and a discrete-time per capita growth rate of zero. At λ < 1, we get a decrease in per capita growth rate. At λ > 1, we get an increase in per capita growth rate. At λ = 0, we get extinction of the species.^[3]

Continuous time version of exponential growth.

Some species have continuous reproduction.

${\displaystyle {\dfrac {dN}{dT}}=rN}$

where ${\displaystyle {\dfrac {dN}{dT}}}$ is the rate of population growth per unit time, r is the maximum per capita growth rate, and N is the population size.

At r > 0, there is an increase in per capita growth rate. At r = 0, the per capita growth rate is zero. At r < 0, there is a decrease in per capita growth rate.

Logistic population growth

“Logistics” comes from the French word logistique, which means “to compute”. Population regulation is a density-dependent process, meaning that population growth rates are regulated by the density of a population. Consider an analogy with a thermostat. When the temperature is too hot, the thermostat turns on the air conditioning to decrease the temperature back to homeostasis. When the temperature is too cold, the thermostat turns on the heater to increase the temperature back to homeostasis. Likewise with density dependence, whether the population density is high or low, population dynamics returns the population density to homeostasis. Homeostasis is the set point, or carrying capacity, defined as K.^[3]

Continuous-time model of logistic growth

${\displaystyle {\dfrac {dN}{dT}}=rN{\Big (}1-{\dfrac {N}{K}}{\Big )}}$

where ${\displaystyle {\Big (}1-{\dfrac {N}{K}}{\Big )}}$ is the density dependence, N is the number in the population, K is the set point for homeostasis and the carrying capacity. In this logistic model, population growth rate is highest at 1/2 K and the population growth rate is zero around K. The optimum harvesting rate is a close rate to 1/2 K where population will grow the fastest. Above K, the population growth rate is negative. The logistic models also show density dependence, meaning the per capita population growth rates decline as the population density increases. In the wild, you can't get these patterns to emerge without simplification. Negative density dependence allows for a population that overshoots the carrying capacity to decrease back to the carrying capacity, K.^[3]

According to R/K selection theory organisms may be specialised for rapid growth, or stability closer to carrying capacity.

Discrete time logistical model

${\displaystyle N_{t+1}=N_{t}+rN_{t}(1-{N_{t}/K})}$

This equation uses r instead of λ because per capita growth rate is zero when r = 0. As r gets very high, there are oscillations and deterministic chaos.^[3] Deterministic chaos is large changes in population dynamics when there is a very small change in r. This makes it hard to make predictions at high r values because a very small r error results in a massive error in population dynamics.

Population is always density dependent. Even a severe density independent event cannot regulate populate, although it may cause it to go extinct.

Not all population models are necessarily negative density dependent. The Allee effect allows for a positive correlation between population density and per capita growth rate in communities with very small populations. For example, a fish swimming on its own is more likely to be eaten than the same fish swimming among a school of fish, because the pattern of movement of the school of fish is more likely to confuse and stun the predator.^[3]

Individual-based models

Cellular automata are used to investigate mechanisms of population dynamics. Here are relatively simple models with one and two species.

Logical deterministic individual-based cellular automata model of single species population growth

 

Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource

Fisheries and wildlife management

In fisheries and wildlife management, population is affected by three dynamic rate functions.

• Natality or birth rate, often recruitment, which means reaching a certain size or reproductive stage. Usually refers to the age a fish can be caught and counted in nets.
• Population growth rate, which measures the growth of individuals in size and length. More important in fisheries, where population is often measured in biomass.
• Mortality, which includes harvest mortality and natural mortality. Natural mortality includes non-human predation, disease and old age.

If N₁ is the number of individuals at time 1 then

${\displaystyle N_{1}=N_{0}+B-D+I-E}$

where N₀ is the number of individuals at time 0, B is the number of individuals born, D the number that died, I the number that immigrated, and E the number that emigrated between time 0 and time 1.
If we measure these rates over many time intervals, we can determine how a population's density changes over time. Immigration and emigration are present, but are usually not measured.

All of these are measured to determine the harvestable surplus, which is the number of individuals that can be harvested from a population without affecting long-term population stability or average population size. The harvest within the harvestable surplus is termed "compensatory" mortality, where the harvest deaths are substituted for the deaths that would have occurred naturally. Harvest above that level is termed "additive" mortality, because it adds to the number of deaths that would have occurred naturally. These terms are not necessarily judged as "good" and "bad," respectively, in population management. For example, a fish & game agency might aim to reduce the size of a deer population through additive mortality. Bucks might be targeted to increase buck competition, or does might be targeted to reduce reproduction and thus overall population size.

For the management of many fish and other wildlife populations, the goal is often to achieve the largest possible long-run sustainable harvest, also known as maximum sustainable yield (or MSY). Given a population dynamic model, such as any of the ones above, it is possible to calculate the population size that produces the largest harvestable surplus at equilibrium.^[4] While the use of population dynamic models along with statistics and optimization to set harvest limits for fish and game is controversial among scientists,^[5] it has been shown to be more effective than the use of human judgment in computer experiments where both incorrect models and natural resource management students competed to maximize yield in two hypothetical fisheries.^[6][7] To give an example of a non-intuitive result, fisheries produce more fish when there is a nearby refuge from human predation in the form of a nature reserve, resulting in higher catches than if the whole area was open to fishing.^[8][9]

For control applications

Population dynamics have been widely used in several control theory applications. With the use of evolutionary game theory, population games are broadly implemented for different industrial and daily-life contexts. Mostly used in multiple-input-multiple-output (MIMO) systems, although they can be adapted for use in single-input-single-output (SISO) systems. Some examples of applications are military campaigns, resource allocation for water distribution, dispatch of distributed generators, lab experiments, transport problems, communication problems, among others. Furthermore, with the adequate contextualization of industrial problems, population dynamics can be an efficient and easy-to-implement solution for control-related problems. Multiple academic research has been and is continuously carried out.

Evolutionary economics

From Wikipedia, the free encyclopedia

Evolutionary economics is part of mainstream economics as well as a heterodox school of economic thought that is inspired by evolutionary biology. Much like mainstream economics, it stresses complex interdependencies, competition, growth, structural change, and resource constraints but differs in the approaches which are used to analyze these phenomena.

Evolutionary economics deals with the study of processes that transform economy for firms, institutions, industries, employment, production, trade and growth within, through the actions of diverse agents from experience and interactions, using evolutionary methodology^[3][4]. Evolutionary economics analyses the unleashing of a process of technological and institutional innovation by generating and testing a diversity of ideas which discover and accumulate more survival value for the costs incurred than competing alternatives. The evidence suggests that it could be adaptive efficiency that defines economic efficiency. Mainstream economic reasoning begins with the postulates of scarcity and rational agents (that is, agents modeled as maximizing their individual welfare), with the "rational choice" for any agent being a straightforward exercise in mathematical optimization. There has been renewed interest in treating economic systems as evolutionary systems in the developing field of Complexity economics.^{[citation needed]}

Evolutionary economics does not take the characteristics of either the objects of choice or of the decision-maker as fixed. Rather its focus is on the non-equilibrium processes that transform the economy from within and their implications^[5][6]. The processes in turn emerge from actions of diverse agents with bounded rationality who may learn from experience and interactions and whose differences contribute to the change. The subject draws more recently on evolutionary game theory^[7] and on the evolutionary methodology of Charles Darwin and the non-equilibrium economics principle of circular and cumulative causation. It is naturalistic in purging earlier notions of economic change as teleological or necessarily improving the human condition.^[8]

A different approach is to apply evolutionary psychology principles to economics which is argued to explain problems such as inconsistencies and biases in rational choice theory. Basic economic concepts such as utility may be better viewed as due to preferences that maximized evolutionary fitness in the ancestral environment but not necessarily in the current one.^[9]

Predecessors

In the mid-19th century, Karl Marx presented a schema of stages of historical development, by introducing the notion that human nature was not constant and was not determinative of the nature of the social system; on the contrary, he made it a principle that human behavior was a function of the social and economic system in which it occurred.

Marx based his theory of economic development on the premise of developing economic systems; specifically, over the course of history superior economic systems would replace inferior ones. Inferior systems were beset by internal contradictions and inefficiencies that make them incapable of surviving over the long term. In Marx's scheme, feudalism was replaced by capitalism, which would eventually be superseded by socialism.^[10]

At approximately the same time, Charles Darwin developed a general framework for comprehending any process whereby small, random variations could accumulate and predominate over time into large-scale changes that resulted in the emergence of wholly novel forms ("speciation").

This was followed shortly after by the work of the American pragmatic philosophers (Peirce, James, Dewey) and the founding of two new disciplines, psychology and anthropology, both of which were oriented toward cataloging and developing explanatory frameworks for the variety of behavior patterns (both individual and collective) that were becoming increasingly obvious to all systematic observers. The state of the world converged with the state of the evidence to make almost inevitable the development of a more "modern" framework for the analysis of substantive economic issues.

Veblen (1898)

Thorstein Veblen (1898) coined the term "evolutionary economics" in English. He began his career in the midst of this period of intellectual ferment, and as a young scholar came into direct contact with some of the leading figures of the various movements that were to shape the style and substance of social sciences into the next century and beyond. Veblen saw the need for taking account of cultural variation in his approach; no universal "human nature" could possibly be invoked to explain the variety of norms and behaviors that the new science of anthropology showed to be the rule, rather than the exception. He emphasised the conflict between "industrial" and "pecuniary" or ceremonial values and this Veblenian dichotomy was interpreted in the hands of later writers as the "ceremonial / instrumental dichotomy" (Hodgson 2004);

Veblen saw that every culture is materially based and dependent on tools and skills to support the "life process", while at the same time, every culture appeared to have a stratified structure of status ("invidious distinctions") that ran entirely contrary to the imperatives of the "instrumental" (read: "technological") aspects of group life. The "ceremonial" was related to the past, and conformed to and supported the tribal legends; "instrumental" was oriented toward the technological imperative to judge value by the ability to control future consequences. The "Veblenian dichotomy" was a specialized variant of the "instrumental theory of value" due to John Dewey, with whom Veblen was to make contact briefly at the University of Chicago.

Arguably the most important works by Veblen include, but are not restricted to, his most famous works (The Theory of the Leisure Class; The Theory of Business Enterprise), but his monograph Imperial Germany and the Industrial Revolution and the 1898 essay entitled Why is Economics not an Evolutionary Science have both been influential in shaping the research agenda for subsequent generations of social scientists. TOLC and TOBE together constitute an alternative construction to the neoclassical marginalist theories of consumption and production, respectively.

Both are founded on his dichotomy, which is at its core a valuational principle. The ceremonial patterns of activity are not bound to any past, but to one that generated a specific set of advantages and prejudices that underlie the current institutions. "Instrumental" judgments create benefits according to a new criterion, and therefore are inherently subversive. This line of analysis was more fully and explicitly developed by Clarence E. Ayres of the University of Texas at Austin from the 1920s.

A seminal article by Armen Alchian (1950) argued for adaptive success of firms faced with uncertainty and incomplete information replacing profit maximization as an appropriate modeling assumption.^[11] Kenneth Boulding was one of the advocates of the evolutionary methods in social science, as is evident from Kenneth Boulding's Evolutionary Perspective. Kenneth Arrow, Ronald Coase and Douglass North are some of the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel winners who are known for their sympathy to the field.

More narrowly the works Jack Downie^[12] and Edith Penrose^[13] offer many insights for those thinking about evolution at the level of the firm in an industry.

Joseph Schumpeter, who lived in the first half of 20th century, was the author of the book The Theory of Economic Development (1911, transl. 1934). It is important to note that for the word development he used in his native language, the German word "Entwicklung", which can be translated as development or evolution. The translators of the day used the word "development" from the French "développement", as opposed to "evolution" as this was used by Darwin. (Schumpeter, in his later writings in English as a professor at Harvard, used the word "evolution".) The current term in common use is economic development.

In Schumpeter's book he proposed an idea radical for its time: the evolutionary perspective. He based his theory on the assumption of usual macroeconomic equilibrium, which is something like "the normal mode of economic affairs". This equilibrium is being perpetually destroyed by entrepreneurs who try to introduce innovations. A successful introduction of an innovation (i.e. a disruptive technology) disturbs the normal flow of economic life, because it forces some of the already existing technologies and means of production to lose their positions within the economy.^{[citation needed]}

Present state of discussion

One of the major contributions to the emerging field of evolutionary economics has been the publication of An Evolutionary Theory of Economic Changby Richard Nelson and Sidney G. Winter. These authors have focused mostly on the issue of changes in technology and routines, suggesting a framework for their analysis. If the change occurs constantly in the economy, then some kind of evolutionary process must be in action, and there has been a proposal that this process is Darwinian in nature.

Then, mechanisms that provide selection, generate variation and establish self-replication, must be identified. The authors introduced the term 'steady change' to highlight the evolutionary aspect of economic processes and contrast it with the concept of 'steady state' popular in classical economics.^[14] Their approach can be compared and contrasted with the population ecology or organizational ecology approach in sociology: see Douma & Schreuder (2013, chapter 11).

Milton Friedman proposed that markets act as major selection vehicles. As firms compete, unsuccessful rivals fail to capture an appropriate market share, go bankrupt and have to exit.^[15] The variety of competing firms is both in their products and practices, that are matched against markets. Both products and practices are determined by routines that firms use: standardized patterns of actions implemented constantly. By imitating these routines, firms propagate them and thus establish inheritance of successful practices.^[16][17] A general theory of this process has been proposed by Kurt Dopfer, John Foster and Jason Potts as the micro meso macro framework.^[18]

Economic processes, as part of life processes, are intrinsically evolutionary^[19]. From the evolutionary equation that describe life processes, an analytical formula on the main factors of economic processes, such as fixed cost and variable cost, can be derived. The economic return, or competitiveness, of economic entities of different characteristics under different kinds of environment can be calculated.^[20] The change of environment causes the change of competitiveness of different economic entities and systems. This is the process of evolution of economic systems.

In recent years, evolutionary models have been used to assist decision making in applied settings and find solutions to problems such as optimal product design and service portfolio diversification.^[21]

Evolutionary psychology

A different approach is to apply evolutionary psychology principles to economics which is argued to explain problems such as inconsistencies and biases in rational choice theory. A basic economic concept such as utility may be better viewed as due to preferences that maximized evolutionary fitness in the ancestral environment but not necessarily in the current one. Loss aversion may be explained as being rational when living at subsistence level where a reduction of resources may have meant death and it thus may have been rational to place a greater value on losses than on gains.^[9]

People are sometimes more cooperative and altruistic than predicted by economic theory which may be explained by mechanisms such as reciprocal altruism and group selection for cooperative behavior. An evolutionary approach may also explain differences between groups such as males being less risk-averse than females since males have more variable reproductive success than females. While unsuccessful risk-seeking may limit reproductive success for both sexes, males may potentially increase their reproductive success much more than females from successful risk-seeking. Frequency-dependent selection may explain why people differ in characteristics such as cooperative behavior with cheating becoming an increasingly less successful strategy as the numbers of cheaters increase.^[9]

Another argument is that humans have a poor intuitive grasp of the economics of the current environment which is very different from the ancestral environment. The ancestral environment likely had relatively little trade, division of labor, and capital goods. Technological change was very slow, wealth differences were much smaller, and possession of many available resources were likely zero-sum games where large inequalities were caused by various forms of exploitation. Humans therefore may have poor intuitive understanding the benefits of free trade (causing calls for protectionism), the value of capital goods (making the labor theory of value appealing), and may intuitively undervalue the benefits of technological development.^[9]

There may be a tendency to see the number of available jobs as a zero-sum game with the total number of jobs being fixed which causes people to not realize that minimum wage laws reduce the number of jobs or to believe that an increased number of jobs in other nations necessarily decreases the number of jobs in their own nation. Large income inequality may easily be viewed as due to exploitation rather than as due to individual differences in productivity. This may easily cause poor economic policies, especially since individual voters have few incentives to make the effort of studying societal economics instead of relying on their intuitions since an individual's vote counts for so little and since politicians may be reluctant to take a stand against intuitive views that are incorrect but widely held.^[9]

Cost-Benefit Reform at the EPA Under Obama, the EPA juked the numbers to justify costly regulation.

By The Editorial Board
June 6, 2018, Wall Street Journal
Appeared in the June 7, 2018, print edition.
Original link:  https://junkscience.com/2018/06/more-winning-epa-administrator-pruitt-proposes-cost-benefit-analysis-reform/#more-93974

Barack Obama’s Environmental Protection Agency jammed through an average of 565 new rules each year during the Obama Presidency, imposing the highest regulatory costs of any agency. It pulled off this regulatory spree in part by gaming cost-benefit analysis to downplay the consequences of its major environmental rules. The Trump Administration has already rolled back some of this overregulation, and now Administrator Scott Pruitt wants to stop the EPA’s numerical shenanigans, too.

On Thursday the EPA will take the first step toward a comprehensive cost-benefit reform by issuing an advance notice of proposed rule-making. After weighing public input, EPA will propose a rule establishing an agency-wide standard for how regulations are assessed. The reform would make it easier for Americans and their elected representatives to see whether more regulation is truly justifiable.

The EPA has a statutory obligation to look at the costs and benefits of many proposed rules. That responsibility has been reinforced by executive orders and court rulings. But while all three branches of government have supported such assessments, they leave the EPA broad discretion. Enter the Obama Administration, which saw the chance to add additional considerations to the cost-benefit equation.

By introducing “social costs” and “social benefits,” the EPA began factoring in speculation about how regulatory inaction would affect everything from rising sea levels to pediatric asthma. EPA optimists even included their guesses about how domestic regulations could have a global impact. Meanwhile, the agency ignored best practices from the Office of Management and Budget, juking the numbers to raise the cost of carbon emissions.

This proved as politically useful as it was scientifically imprecise. Months before introducing the Clean Power Plan, the EPA suddenly raised the social cost of a ton of carbon emissions to an average of $36 from $21. Before it embarked on new oil and gas regulations, the EPA put the social cost of methane at an average of $1,100 per ton.

At White House direction, the Trump EPA recalculated those figures last year to include only demonstrable domestic benefits. The social cost estimates dropped to an average of $5 per ton of carbon and $150 per ton of methane. That made a big difference in the cost-benefit analysis. While the Obama Administration claimed the Clean Power Plan would yield up to $43 billion in net benefits by 2030, the Trump EPA concluded it would carry a $13 billion net cost.

Another statistical sleight of hand involves the Mercury and Air Toxics Standards. The regulation’s stated purpose was to reduce mercury pollution, but the EPA added the rule’s potential to decrease dust. That was irrelevant to the central question of whether it was worthwhile to regulate mercury as proposed. But without the erroneous co-benefits, EPA would find such regulations tougher to justify.

On his first day in office, Mr. Pruitt said his goal was to protect the environment and the economy, and that “we don’t have to choose between the two.” His many ethics controversies have distracted from that mission, but this cost-benefit reform is a welcome return.

The regulatory specifics will be hashed out in the coming months, but there’s real potential here to curb the distortions that mask bad policy. If Mr. Pruitt succeeds, future cost-benefit analyses will be more consistent and transparent. The reform would help to ensure regulation is based on sound scientific analysis instead of wishful bureaucratic thinking.