demsetz thesis evolution property rights

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Demsetz thesis evolution property rights

But in some regions the designs will yield higher levels of fitness and peaks and valleys emerge. Because neighboring bit strings are very similar to each other, though not identical, they will tend to display approximately similar levels of fitness, thus producing smoothly rounded hills. If an essential bit is flipped, however, abrupt precipices and sharp spikes can result. As we will see, the shape of the landscape gives us information concerning the characteristics of the search problem we are investigating and the ability of different search strategies to find good solutions to those problems.

In the case of the evolution of property rights or other social constructs, the concept of fitness is not as clear as it is in biology. Here, fitness also refers to the capacity of a design of property rights to replicate and persist over time. Note that given that the unit of selection is the design of the property rights and not the group that uses those rights, fitness refers to the tendency of that design being kept in place rather than being changed for another bundle of property rights.

Of course, if the extant bundle is highly dysfunctional, this will affect the welfare of the group, which may grow and prosper at a slower rate, or may even be conquered by other social groups. So the fitness of the property rights is related to the welfare of the society, but it is important to keep in mind that when we mention fitness in this Essay it will refer to the fitness of the bundle for the sake of its own perpetuation over time.

Suppose that Figure 2 represents a fitness landscape for property rights for a given good in a specific society. The original set of rights will emerge by spontaneous order at a given point in the landscape. Even though there might be some purpose and intentionality in the design, what actually emerges does so in a nonteleological fashion as the process is subject to error, surprise, and serendipity.

Joel Mokyr shows that even in the case of technological evolution, where there is obviously purposeful action, the innovations that emerge cannot be seen as the result of a closely controlled process. The original bundle of rights that emerges can only appear in a section of the landscape that exhibits at least a minimal level of fitness.

He states that:. Evolution may be impossible without the privilege of working with systems that already exhibit internal order, with fitness landscapes already naturally tuned so that natural selection can get a foothold and do its job. And here, I think, may be an essential tie between self-organization and selection. Self-organization may be the precondition of evolvability itself. Note then, that the position on the landscape where the original bundle emerges will define in many ways the nature of the task that the system faces to find fit design.

If the original bundle happens to emerge near a high peak, and the search strategy is an adaptive walk that always takes the steepest path available, then the task will be relatively easy and the result will be highly fit. If the starting point is near a low peak, that same strategy will get the system stuck in a suboptimal solution from which it will be hard to escape, even in the presence of nearby better solutions.

Thus the process is highly path dependent. There are a large number of strategies for searching large landscapes for fit design, such as random jumps, adaptive walks, greedy algorithms, and simulated annealing, among others. In many real world applications, evolution—a process of variation, selection, and replication—turns out to be the dominant strategy. Evolution is ubiquitous because it is particularly well suited for search in rugged landscapes such as that in Figure 1, which characterize the problem typically faced in biology, culture, language, technology, and property rights.

Evolution can be seen as a combination of an adaptive walk that seeks to move uphill, with short random jumps produced by recombination and mutation, which allow the system to escape from inferior peaks so as to explore other sections of the landscape for superior designs.

The strength of evolution is its ability to balance exploration with exploitation. There is, however, a tradeoff that must be considered. It is possible that the current point is a local maximum and there may be preferable peaks nearby that can be reached through some viable variation.

It would thus be wise to dedicate some efforts to exploring the landscape for fitter designs. Exploitation takes place as fit designs that have been selected by the environmental pressures replicate. Yet, because this replication happens with errors, i. Mutation and sexual recombination can be seen as a jump from one point in the landscape, possibly a local peak, to another noncontiguous point.

Furthermore, evolution is such that this balancing act dynamically adjusts itself to circumstances, exploiting more when the current set up is particularly fit for the environment and exploring more when fitness decreases. Smooth, Rugged, and Random Fitness Landscapes.

Now we are in the position to ask what determines the shape of the landscape the number, heights, and disposition of the peaks in the systems we are concerned with. When will there be a landscape with a single smooth peak that can easily be climbed, and when will there be a large number of distributed smaller peaks confounding the search for the best combination? As K increases, some of the features are coupled with other features, so that the total fitness contribution of one is determined by its own contribution as well as the indirect contribution of the K other features.

This means that there are constraints and trade-offs in changing any single bit. The effect on the landscape is that it becomes increasingly rugged as K increases, with more peaks and lower levels of fitness all around, as in the middle picture in Figure 3. It turns out that, in practical applications, even low levels of K are already sufficient to cause ruggedness with all the obstacles it creates for the search of fit design.

If K reaches very high levels, close to N-1 , then the landscape becomes so intertwined that it is effectively random, as in the right picture in Figure 3. Figure 3. Change in Landscape as K Increases It is these conflicting constraints that make the landscape rugged and multipeaked.

Because so many constrains are in conflict, there is a large number of rather modest compromise solutions rather than an obvious superb solution. There are in other words, many local peaks with very low altitudes. Because landscapes are more rugged, adaptation becomes harder. Interactions of Sticks in the Bundle. Henry Smith, in a critique to the bundle-of-rights metaphor, has made the point that property rights are a complex adaptive system, in which the whole is more than the sum of the parts:.

Wetness is an emergent property of water. So with property. In principle the bundle theory could take this into account, but it typically does not. Instead, the metaphor of the bundle of sticks is used to imply precisely the opposite. In a bundle of sticks the sticks do not interact; you can add or subtract them at will, and still you will have a bundle with roughly the same properties. In the Demsetz view, property rights always adjust optimally given the circumstances relative prices through more-or-less intentional design, moving from open access to commons to private property with clear directionality.

Thus, Demsetz implicitly assumes that the bundles of rights have no epistatic couplings, i. Assuredly, there may very well be many cases in which the different features of the bundle are focused and detachable so that the evolution of property rights would be optimal and predictable. But as a general rule it is probably the case that several sticks in the bundle are interconnected, so that the problem of evolving property rights is complex and doomed by its very nature to reach only compromise solutions through often tortuous paths.

This fits the description of the evolution of property rights in the United States by Anderson and Hill, 43 as well as that of the United States, Australia, and Brazil by Alston, Harris, and Mueller, 44 all of which experienced significant rent dissipation and suboptimal solutions. Coevolution and Dancing Landscapes. But there is a further complication. Until now we have assumed that the fitness landscape is fixed, so that a given bundle of rights always produces the same level of fitness. But this is clearly not realistic.

As exogenous shocks change relative prices and open up new opportunities, the old bundle of rights might no longer be a good way of organizing property. If the fitness of each potential bundle of right changes when a shock occurs, then the fitness landscape dances, with former peaks potentially plunging and what were valleys possibly rising to new heights.

When this happens, what was a good design may no longer be able to deal with the new conflicts that arise and a new fitter bundle may or may not evolve. Consider, for example, the disruption brought about to intellectual property by the arrival of the Internet and easy digitalization of content. In biological systems the landscapes also dance in response to exogenous shocks, such as a giant meteorite, or global warming, but they also do so for endogenous reasons.

Because each species can have several predators and prey, as well as a host of symbiotic relationships, the fitness of any given design depends on the designs of these related species. Therefore, when one species undergoes variation to climb in its own landscape, the related species will find that their own landscapes shift, as their current design now yields a different level of fitness.

If hawks evolve better eyesight, rabbits will find that their camouflage no longer contributes as much towards their survival and replication as it originally did. This process of coevolution repeats continuously. Coevolution is modeled in the NK approach by allowing some bits in the bit string of a given species to be coupled to some bits in the bit strings of other species.

Thus, besides having N bits and K internal epistatic couplings within the same bit string, there are now C external couplings with other species, and this can be the case for S other species. This extension is known as the NK C Model. Note that each trait in the first species is affected by two traits in the second species and vice versa. Thus, the fitness contribution of the first trait in species 2, for example, is determined by itself, by the third trait in its own string, and by the first two traits in species.

Figure 4. Whereas K determines the ruggedness of the landscapes, C and S affect the extent and rate of the dancing of the landscape. The more species that are coupled higher S and the greater the extent of external epistatis greater C the more the landscapes will dance, with changes in one bit sending tectonic shifts across all the landscapes thus linked.

It is counterintuitive that under coevolution the greater the number of internal couplings K , the more static the dynamics of the landscape will be. But in the NK C Model, the result is inverted. For a given level of S and C , that is, for a given rate of dancing of the landscape, low values of K in a given landscape are associated with chaotic behavior in the form of continual change of the bit string, and high values of K lead to stable behavior.

If the landscape starts to dance, with the single peak becoming a single valley, heaving up and down in different regions of the landscape, the species is never able to evolve a good solution. When a peak pops up in a given region of the landscape, the species starts evolving in that direction, but, before the peak is reached, the landscape changes and the peak pops up somewhere else. So the species is always chasing the elusive peak in a chaotic fashion. The peaks are high, but are never reached, so the average fitness of the species is relatively low.

When K is high, the landscape is random and full of spikes. If the landscape starts dancing, with the spikes stabbing up and down all over the place, the species will not be prompted to evolve very far from where it already is, as there is always a nearby peak. But the peaks are relatively low given all the internal constraints. The evolutionary behavior thus resembles an evolutionary stable strategy.

In this situation there is a rugged, but not random landscape, with several rolling peaks. When the landscape dances, the species finds that its previous design is no longer fit and it tries to evolve towards a higher peak. Contrary to the single-peak case, several peaks pop up relatively nearby, such that it can actually reach and climb before the landscape changes again. Thus, the fitness when K is intermediate is on average relatively high. Kauffman describes this result as follows:.

The highest average fitness occurs precisely at the transition from order to chaos. Deep in the ordered regime, fitness peaks are low because of conflicting constraints. Deep in the chaotic regime, fitness peaks are high, but there are too few and move too rapidly to be climbed.

The transition regime occurs precisely at that point on the axis where the peaks can just be climbed on the time scale available. Here the peaks are simultaneously the highest possible and still attainable in the time available. The importance of this result is that it implies that coevolutionary relationships themselves evolve.

If there is a combination of K , S , and C that tends to result in higher average fitness, then it must be the case that those sorts of relationships will tend to predominate. Evolution leads to webs of relationships between predators, prey, and other interrelated species that are fitter than other possible relationships given the environment. How can coevolution be understood in the context of the evolution of property rights?

Given that the notion of a bundle of rights was in part created to allow property to be simultaneously held by multiple parties, it is tempting to think of different holders of the attributes of a given bundle as the participants of the coevolution; actions by one might affect the welfare of the other.

But this would not be a proper analogy, for what is being evolved—the unit of selection—are not the owners but rather the property rights themselves; that is, the information contained in that set of instructions of how to act towards the property. Consider instead that in the context where property rights exist there are other laws, rules, and institutions that coexist affecting the same society.

Rule of law, such as contract law, codes of conduct, institutions for resolving conflicts, and many others, can all be thought of as evolving units that can similarly be expressed as a set of instructions and consequently be portrayed as having their own fitness landscapes. It is reasonable to assume that the functioning of any bundle of property rights will be influenced by many of these other rules.

In other words, property rights not only have internal epistatic links, but also external epistatic links, C to S other rules, which therefore coevolve with the property rights in an institutional ecosystem. If this coevolution is in fact taking place, then the main result from the NK C Model should carry over to the case of property rights. This result would be stated as follows: The evolution of property rights in a context where these rights are embedded in a web or ecosystem of laws and institutions will tend to give rise to property rights that have intermediate levels of interrelatedness among the sticks in the bundle.

Because property rights with moderate levels of interrelatedness have higher fitness than either the bundles of independent rights or the bundles of maximally connected rights, we should expect bundles with these characteristics to prevail over time. By giving the means for societies to better deal with conflicts and change, this class of property rights will be replicated and should dominate. The logic of dancing landscapes implies that the property rights we observe are not optimal, in the sense that higher peaks exist and may have been reached at certain points in time.

Similarly, in many instances the property rights will be at points of low fitness. But in a context of continuous and unpredictable change, the process through which the bundles adapt and change is perhaps the best that can be done, and is itself quite remarkable. We used fitness landscapes and the classic theory of evolution to suggest that the evolution of property rights is highly complex and contextual.

It is not as simple as changing relative prices lead unilaterally to a better set of property rights. It depends on the relationship across the attributes of the property rights along with the belief structure in which they are embedded. Beyene, Fekadu, Kim, Annette M. More about this item Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions.

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LITERATURE REVIEW IN MOBILE LEARNING IN THE UNIVERSITY CONTEXT

In fact, however, property regimes are not static but change over time. Given the assumption of fixed property that otherwise prevails in economic literature, explaining the evolution of property rights is one of the great challenges for the economic analysis of law. Yet for all its deserved fame, the article contains at best a sketch of a theory and offers only anecdotal evidence by way of support.

The purpose of the conference was to reexamine the Demsetz thesis, consider possible alternatives or elaborations to it, and develop further empirical evidence either to confirm or disconfirm it. The articles in this volume, including an afterword by Demsetz, are the outgrowth of the papers presented at that conference. The Demsetz thesis can be seen as an anticipation of the idea that the common law evolves toward efficient rules. Demsetz hypothesized that property rights emerge when the social benefits of establishing such rights exceed their social costs.

In effect, he suggested that legal rules regarding resources change over time along a path that produces net benefits to the relevant community. Originally published in the Journal of Legal Studies, Vol. S2, p. S, Legal Stud. S The original bundle of rights that emerges can only appear in a section of the landscape that exhibits at least a minimal level of fitness. He states that:. Evolution may be impossible without the privilege of working with systems that already exhibit internal order, with fitness landscapes already naturally tuned so that natural selection can get a foothold and do its job.

And here, I think, may be an essential tie between self-organization and selection. Self-organization may be the precondition of evolvability itself. Note then, that the position on the landscape where the original bundle emerges will define in many ways the nature of the task that the system faces to find fit design. If the original bundle happens to emerge near a high peak, and the search strategy is an adaptive walk that always takes the steepest path available, then the task will be relatively easy and the result will be highly fit.

If the starting point is near a low peak, that same strategy will get the system stuck in a suboptimal solution from which it will be hard to escape, even in the presence of nearby better solutions. Thus the process is highly path dependent. There are a large number of strategies for searching large landscapes for fit design, such as random jumps, adaptive walks, greedy algorithms, and simulated annealing, among others.

In many real world applications, evolution—a process of variation, selection, and replication—turns out to be the dominant strategy. Evolution is ubiquitous because it is particularly well suited for search in rugged landscapes such as that in Figure 1, which characterize the problem typically faced in biology, culture, language, technology, and property rights. Evolution can be seen as a combination of an adaptive walk that seeks to move uphill, with short random jumps produced by recombination and mutation, which allow the system to escape from inferior peaks so as to explore other sections of the landscape for superior designs.

The strength of evolution is its ability to balance exploration with exploitation. There is, however, a tradeoff that must be considered. It is possible that the current point is a local maximum and there may be preferable peaks nearby that can be reached through some viable variation. It would thus be wise to dedicate some efforts to exploring the landscape for fitter designs.

Exploitation takes place as fit designs that have been selected by the environmental pressures replicate. Yet, because this replication happens with errors, i. Mutation and sexual recombination can be seen as a jump from one point in the landscape, possibly a local peak, to another noncontiguous point.

Furthermore, evolution is such that this balancing act dynamically adjusts itself to circumstances, exploiting more when the current set up is particularly fit for the environment and exploring more when fitness decreases. Smooth, Rugged, and Random Fitness Landscapes. Now we are in the position to ask what determines the shape of the landscape the number, heights, and disposition of the peaks in the systems we are concerned with. When will there be a landscape with a single smooth peak that can easily be climbed, and when will there be a large number of distributed smaller peaks confounding the search for the best combination?

As K increases, some of the features are coupled with other features, so that the total fitness contribution of one is determined by its own contribution as well as the indirect contribution of the K other features. This means that there are constraints and trade-offs in changing any single bit. The effect on the landscape is that it becomes increasingly rugged as K increases, with more peaks and lower levels of fitness all around, as in the middle picture in Figure 3.

It turns out that, in practical applications, even low levels of K are already sufficient to cause ruggedness with all the obstacles it creates for the search of fit design. If K reaches very high levels, close to N-1 , then the landscape becomes so intertwined that it is effectively random, as in the right picture in Figure 3. Figure 3. Change in Landscape as K Increases It is these conflicting constraints that make the landscape rugged and multipeaked.

Because so many constrains are in conflict, there is a large number of rather modest compromise solutions rather than an obvious superb solution. There are in other words, many local peaks with very low altitudes.

Because landscapes are more rugged, adaptation becomes harder. Interactions of Sticks in the Bundle. Henry Smith, in a critique to the bundle-of-rights metaphor, has made the point that property rights are a complex adaptive system, in which the whole is more than the sum of the parts:. Wetness is an emergent property of water. So with property. In principle the bundle theory could take this into account, but it typically does not. Instead, the metaphor of the bundle of sticks is used to imply precisely the opposite.

In a bundle of sticks the sticks do not interact; you can add or subtract them at will, and still you will have a bundle with roughly the same properties. In the Demsetz view, property rights always adjust optimally given the circumstances relative prices through more-or-less intentional design, moving from open access to commons to private property with clear directionality.

Thus, Demsetz implicitly assumes that the bundles of rights have no epistatic couplings, i. Assuredly, there may very well be many cases in which the different features of the bundle are focused and detachable so that the evolution of property rights would be optimal and predictable. But as a general rule it is probably the case that several sticks in the bundle are interconnected, so that the problem of evolving property rights is complex and doomed by its very nature to reach only compromise solutions through often tortuous paths.

This fits the description of the evolution of property rights in the United States by Anderson and Hill, 43 as well as that of the United States, Australia, and Brazil by Alston, Harris, and Mueller, 44 all of which experienced significant rent dissipation and suboptimal solutions. Coevolution and Dancing Landscapes. But there is a further complication. Until now we have assumed that the fitness landscape is fixed, so that a given bundle of rights always produces the same level of fitness.

But this is clearly not realistic. As exogenous shocks change relative prices and open up new opportunities, the old bundle of rights might no longer be a good way of organizing property. If the fitness of each potential bundle of right changes when a shock occurs, then the fitness landscape dances, with former peaks potentially plunging and what were valleys possibly rising to new heights. When this happens, what was a good design may no longer be able to deal with the new conflicts that arise and a new fitter bundle may or may not evolve.

Consider, for example, the disruption brought about to intellectual property by the arrival of the Internet and easy digitalization of content. In biological systems the landscapes also dance in response to exogenous shocks, such as a giant meteorite, or global warming, but they also do so for endogenous reasons. Because each species can have several predators and prey, as well as a host of symbiotic relationships, the fitness of any given design depends on the designs of these related species.

Therefore, when one species undergoes variation to climb in its own landscape, the related species will find that their own landscapes shift, as their current design now yields a different level of fitness. If hawks evolve better eyesight, rabbits will find that their camouflage no longer contributes as much towards their survival and replication as it originally did. This process of coevolution repeats continuously. Coevolution is modeled in the NK approach by allowing some bits in the bit string of a given species to be coupled to some bits in the bit strings of other species.

Thus, besides having N bits and K internal epistatic couplings within the same bit string, there are now C external couplings with other species, and this can be the case for S other species. This extension is known as the NK C Model. Note that each trait in the first species is affected by two traits in the second species and vice versa. Thus, the fitness contribution of the first trait in species 2, for example, is determined by itself, by the third trait in its own string, and by the first two traits in species.

Figure 4. Whereas K determines the ruggedness of the landscapes, C and S affect the extent and rate of the dancing of the landscape. The more species that are coupled higher S and the greater the extent of external epistatis greater C the more the landscapes will dance, with changes in one bit sending tectonic shifts across all the landscapes thus linked. It is counterintuitive that under coevolution the greater the number of internal couplings K , the more static the dynamics of the landscape will be.

But in the NK C Model, the result is inverted. For a given level of S and C , that is, for a given rate of dancing of the landscape, low values of K in a given landscape are associated with chaotic behavior in the form of continual change of the bit string, and high values of K lead to stable behavior. If the landscape starts to dance, with the single peak becoming a single valley, heaving up and down in different regions of the landscape, the species is never able to evolve a good solution.

When a peak pops up in a given region of the landscape, the species starts evolving in that direction, but, before the peak is reached, the landscape changes and the peak pops up somewhere else. So the species is always chasing the elusive peak in a chaotic fashion. The peaks are high, but are never reached, so the average fitness of the species is relatively low. When K is high, the landscape is random and full of spikes.

If the landscape starts dancing, with the spikes stabbing up and down all over the place, the species will not be prompted to evolve very far from where it already is, as there is always a nearby peak. But the peaks are relatively low given all the internal constraints. The evolutionary behavior thus resembles an evolutionary stable strategy. In this situation there is a rugged, but not random landscape, with several rolling peaks. When the landscape dances, the species finds that its previous design is no longer fit and it tries to evolve towards a higher peak.

Contrary to the single-peak case, several peaks pop up relatively nearby, such that it can actually reach and climb before the landscape changes again. Thus, the fitness when K is intermediate is on average relatively high. Kauffman describes this result as follows:. The highest average fitness occurs precisely at the transition from order to chaos.

Deep in the ordered regime, fitness peaks are low because of conflicting constraints. Deep in the chaotic regime, fitness peaks are high, but there are too few and move too rapidly to be climbed. The transition regime occurs precisely at that point on the axis where the peaks can just be climbed on the time scale available. Here the peaks are simultaneously the highest possible and still attainable in the time available. The importance of this result is that it implies that coevolutionary relationships themselves evolve.

If there is a combination of K , S , and C that tends to result in higher average fitness, then it must be the case that those sorts of relationships will tend to predominate. Evolution leads to webs of relationships between predators, prey, and other interrelated species that are fitter than other possible relationships given the environment. How can coevolution be understood in the context of the evolution of property rights?

Given that the notion of a bundle of rights was in part created to allow property to be simultaneously held by multiple parties, it is tempting to think of different holders of the attributes of a given bundle as the participants of the coevolution; actions by one might affect the welfare of the other. But this would not be a proper analogy, for what is being evolved—the unit of selection—are not the owners but rather the property rights themselves; that is, the information contained in that set of instructions of how to act towards the property.

Consider instead that in the context where property rights exist there are other laws, rules, and institutions that coexist affecting the same society. Rule of law, such as contract law, codes of conduct, institutions for resolving conflicts, and many others, can all be thought of as evolving units that can similarly be expressed as a set of instructions and consequently be portrayed as having their own fitness landscapes.

It is reasonable to assume that the functioning of any bundle of property rights will be influenced by many of these other rules. In other words, property rights not only have internal epistatic links, but also external epistatic links, C to S other rules, which therefore coevolve with the property rights in an institutional ecosystem. If this coevolution is in fact taking place, then the main result from the NK C Model should carry over to the case of property rights.

This result would be stated as follows: The evolution of property rights in a context where these rights are embedded in a web or ecosystem of laws and institutions will tend to give rise to property rights that have intermediate levels of interrelatedness among the sticks in the bundle.

Because property rights with moderate levels of interrelatedness have higher fitness than either the bundles of independent rights or the bundles of maximally connected rights, we should expect bundles with these characteristics to prevail over time. By giving the means for societies to better deal with conflicts and change, this class of property rights will be replicated and should dominate. The logic of dancing landscapes implies that the property rights we observe are not optimal, in the sense that higher peaks exist and may have been reached at certain points in time.

Similarly, in many instances the property rights will be at points of low fitness. But in a context of continuous and unpredictable change, the process through which the bundles adapt and change is perhaps the best that can be done, and is itself quite remarkable. We used fitness landscapes and the classic theory of evolution to suggest that the evolution of property rights is highly complex and contextual.

It is not as simple as changing relative prices lead unilaterally to a better set of property rights. It depends on the relationship across the attributes of the property rights along with the belief structure in which they are embedded. The fitness landscapes provide a heuristic for understanding the process through which property rights evolve as a process of search for designs that are better able to mediate the issues and conflicts encountered given the environment and context.

This model accounts for the fact that property rights change when shocks affect relative prices, but it also allows for current arrangements getting stuck in suboptimal solutions, which is a feature that seems to characterizes many property rights around the world. The number of interdependencies between the different sticks in the bundle of rights is the key determinant of the difficulty in changing property rights optimally and the optimality of the available solutions.

The model shows that because property rights coevolve with other institutions as well as with technological arrangements, in societies where there are neither too few nor too many interdependencies property rights will tend to replicate more and thus prevail over time.

In this Essay, we have only sketched out theoretically how property rights evolution can be analyzed through the fitness landscape framework. The next step is to test this approach against actual historical events to see if our implications are confirmed; for example, by comparing the evolution of property rights to land in colonial America to the evolution of property rights within the United Kingdom. It is not straightforward how this could be done because there is no obvious way to establish the relevant design space of property rights for measuring the fitness of each bundle.

The key is to infer which sticks compose the bundle of rights and associate the bundle within a society to a measure of how well the extant property rights mediate and coordinate the use of resources. Importantly, it is necessary to ascertain the interrelationship among the different sticks in the property rights bundle to each other and to external coevolving institutions and technologies. With suitable measurements or proxies for these variables, the analyst then needs to track the changes in the property rights arrangements over relatively long periods of time to see if they follow the evolutionary process described in the model.

Property rights bundles that have moderate amounts of internal and external interdependencies should prevail over time. A successful application of the model involves both depicting the process of spontaneous order through which the initial set of property rights emerges and the evolutionary process of change that follows thereafter. Terry L.

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The failure of an institution to live up to the ideal is not yet an argument for replacing it. Although warnings about the nirvana fallacy are now a staple of comparative institutional analysis, expecting the total eradication of the nirvana fallacy would probably be an instance of it! As those who met Harold in person or saw him speak, there are few people — much less academics and economists!

One of his favorite sayings was that Ronald Coase got the Nobel Prize for an article that he Harold had written many more times! As a great thinker and a warm human being, Harold is an irreplaceable original, who will be greatly missed. Your response:. Notify me of follow-up comments by email. Notify me of new posts by email. Your Email Address. John Goldberg Henry Smith. Yonathan Arbel.

Aditi Bagchi. Shyam Balganesh. Samuel Beswick. Janet Freilich. Andrew Gold. John Golden. Patrick Goold. Keith Hylton. Daniel Kelly. Greg Klass. Daniel Markovits. Tom Merrill. Anthony J. Ted Sichelman. Henry Smith. Tess Wilkinson-Ryan. And privatization, in turn, leads to more efficient use of the resource by the individuals holding the property rights, with less externalization of the harmful effects of resource use.

This governance can include traditional regulation that draws clearer property rights in the resource and forces cost internalization as well as innovative, less formal regimes, such as monitoring and reporting of resource use, voluntary agreements to internalize certain harms, and other commons management tools.

But a conundrum remains: in some cases, scarcity does not generate regulation or innovative governance, and legal scholarship has called for more empirical testing of the reasons for this anti-Demsetzian response. This oil and gas extraction technique, which has recently boomed in the United States, has identifiable and substantial negative externalities including, for example, air pollution and over-withdrawals of freshwater during droughts.

Yet states and industry actors have not consistently responded with regulations or innovative governance strategies to internalize these externalities. In this Article, we explore the responses of three states experiencing a fracking boom and theorize the reasons for the diverse responses of these states to greater fracking externalities, including responses that do not track the Demsetz theory.

Dana and Hannah J. Law Commons. Advanced Search. Privacy Copyright. Skip to main content.

EXAMPLES REFLECTIVE ESSAYS USING GIBBS MODEL

In effect, he suggested that legal rules regarding resources change over time along a path that produces net benefits to the relevant community. Originally published in the Journal of Legal Studies, Vol. S2, p. S, Legal Stud. S Property Law and Real Estate Commons. Advanced Search. Privacy Copyright. Skip to main content. Faculty Scholarship. Authors Thomas W. Abstract Both conventional price theory and standard economic accounts of tort and contract law assume fixed property rights.

One of the most appealing applications of evolutionary logic to epsitemic substrates has been that of technology and technological change. In evolutionary accounts of the process of variation, selection, and amplification of technology, it is tempting to think of the artifacts that result from invention and innovation as the unit of selection.

This perspective of property rights as a code fits remarkably well with the notion, familiar to law school students, of property as a bundle of rights, where instead of property being a right to a thing, it is a relation between people with respect to a thing and it can be broken down into pieces that have direct consequences. Suppose that there are N different sticks in the bundle and that each component can be present or absent, so that we can write it as a zero or a one.

Thus, a given bundle of rights can be represented as a string of zeros and ones that specifies that particular instruction of what can and cannot be done with the property. This is analogous to a genome, which contains N genes approximately 20, in humans that specify the instructions on how to render a specimen. Each gene can assume different forms or alleles for example, for dark hair or light hair.

Similarly, a stick in the bundle of rights can take many different gradations. But following the literature we simplify by assuming only presence or absence, so as to have a bit string. Given N sticks in the bundle, the total number of possible different bundles is 2 N. If the three sticks in the bundle are 1 the right to use; 2 the right to sell; and 3 the right to manage, then represents the absence of each of these rights, the right to use but not to sell or to manage, and so on.

If N is large then the total number of bundles will be too large to consider each possibility case by case. If one considers only the nine sticks cited above, then the total number of combinations would be If each of the nine definitions were further disaggregated into three more specific rights, then the total number of bundles would be 2 27 —over million combinations.

The power of combinatorics means that the number of possibilities rises very fast. In order to be more precise on what is involved in this task, we will use the notion of fitness landscapes, as their shapes impart information on how likely it is that a good solution will be found. Furthermore, we will show how the shape of the landscape is determined by the characteristics of the property and the environment it is in.

A further complication that must be considered before going to the landscapes is that the contribution of each of the sticks in the bundle towards the total fitness under that bundle might not be independent of the state of the other sticks. This is analogous to the phenomenon of epistasis in genetics, where the effect of one gene depends on the presence of one or more other genes.

Since the usefulness of a given phenotypic trait e. In a similar manner, the different sticks in the bundle of rights can be related in a network of epistatic interactions. For example, if the contribution to total fitness of the right to use is affected by the right to the capital. A bundle of rights with its epistatic couplings can be represented with arrows showing which sticks affect other sticks, as in Figure 1.

The number of couplings is referred to as K. More generally, K can vary from 0 to N These couplings are conflicting constraints, so as K increases from a situation where each stick is independent from the others to a maximally constrained situation where each stick is coupled to every other, the problem of finding high fitness combinations becomes harder and, as we shall see, the level of attainable fitness decreases.

Figure 1. A fitness landscape starts off with a design space of all possible combinations of the set of instructions laid out along a horizontal plane. Each bit string has N-1 neighbors that are one-bit mutations; that is, each neighbor is identical to itself except for one bit that is flipped from 0 to 1 or vice-versa.

As we have seen, even moderately small values of N imply that the design space will be very large, and in many applications, including property rights, it may be astronomical. For each bit string in the design space a corresponding level of fitness can be plotted on the vertical axis, thus creating a fitness landscape, where the peaks represent the most-fit designs see Figure 2, which plots a section of a fitness landscape. In biological applications, fitness refers to the capacity of that design reproducing faster than others so as to prevail in the population.

The fact that fitter designs replicate more than less-fit designs implies that the species will tend to climb towards higher peaks. Most designs will typically provide very low or null fitness so the landscape is flat at zero. But in some regions the designs will yield higher levels of fitness and peaks and valleys emerge.

Because neighboring bit strings are very similar to each other, though not identical, they will tend to display approximately similar levels of fitness, thus producing smoothly rounded hills. If an essential bit is flipped, however, abrupt precipices and sharp spikes can result. As we will see, the shape of the landscape gives us information concerning the characteristics of the search problem we are investigating and the ability of different search strategies to find good solutions to those problems.

In the case of the evolution of property rights or other social constructs, the concept of fitness is not as clear as it is in biology. Here, fitness also refers to the capacity of a design of property rights to replicate and persist over time. Note that given that the unit of selection is the design of the property rights and not the group that uses those rights, fitness refers to the tendency of that design being kept in place rather than being changed for another bundle of property rights.

Of course, if the extant bundle is highly dysfunctional, this will affect the welfare of the group, which may grow and prosper at a slower rate, or may even be conquered by other social groups. So the fitness of the property rights is related to the welfare of the society, but it is important to keep in mind that when we mention fitness in this Essay it will refer to the fitness of the bundle for the sake of its own perpetuation over time.

Suppose that Figure 2 represents a fitness landscape for property rights for a given good in a specific society. The original set of rights will emerge by spontaneous order at a given point in the landscape. Even though there might be some purpose and intentionality in the design, what actually emerges does so in a nonteleological fashion as the process is subject to error, surprise, and serendipity.

Joel Mokyr shows that even in the case of technological evolution, where there is obviously purposeful action, the innovations that emerge cannot be seen as the result of a closely controlled process. The original bundle of rights that emerges can only appear in a section of the landscape that exhibits at least a minimal level of fitness.

He states that:. Evolution may be impossible without the privilege of working with systems that already exhibit internal order, with fitness landscapes already naturally tuned so that natural selection can get a foothold and do its job. And here, I think, may be an essential tie between self-organization and selection. Self-organization may be the precondition of evolvability itself.

Note then, that the position on the landscape where the original bundle emerges will define in many ways the nature of the task that the system faces to find fit design. If the original bundle happens to emerge near a high peak, and the search strategy is an adaptive walk that always takes the steepest path available, then the task will be relatively easy and the result will be highly fit.

If the starting point is near a low peak, that same strategy will get the system stuck in a suboptimal solution from which it will be hard to escape, even in the presence of nearby better solutions. Thus the process is highly path dependent. There are a large number of strategies for searching large landscapes for fit design, such as random jumps, adaptive walks, greedy algorithms, and simulated annealing, among others.

In many real world applications, evolution—a process of variation, selection, and replication—turns out to be the dominant strategy. Evolution is ubiquitous because it is particularly well suited for search in rugged landscapes such as that in Figure 1, which characterize the problem typically faced in biology, culture, language, technology, and property rights.

Evolution can be seen as a combination of an adaptive walk that seeks to move uphill, with short random jumps produced by recombination and mutation, which allow the system to escape from inferior peaks so as to explore other sections of the landscape for superior designs. The strength of evolution is its ability to balance exploration with exploitation. There is, however, a tradeoff that must be considered.

It is possible that the current point is a local maximum and there may be preferable peaks nearby that can be reached through some viable variation. It would thus be wise to dedicate some efforts to exploring the landscape for fitter designs. Exploitation takes place as fit designs that have been selected by the environmental pressures replicate. Yet, because this replication happens with errors, i. Mutation and sexual recombination can be seen as a jump from one point in the landscape, possibly a local peak, to another noncontiguous point.

Furthermore, evolution is such that this balancing act dynamically adjusts itself to circumstances, exploiting more when the current set up is particularly fit for the environment and exploring more when fitness decreases. Smooth, Rugged, and Random Fitness Landscapes.

Now we are in the position to ask what determines the shape of the landscape the number, heights, and disposition of the peaks in the systems we are concerned with. When will there be a landscape with a single smooth peak that can easily be climbed, and when will there be a large number of distributed smaller peaks confounding the search for the best combination?

As K increases, some of the features are coupled with other features, so that the total fitness contribution of one is determined by its own contribution as well as the indirect contribution of the K other features. This means that there are constraints and trade-offs in changing any single bit. The effect on the landscape is that it becomes increasingly rugged as K increases, with more peaks and lower levels of fitness all around, as in the middle picture in Figure 3. It turns out that, in practical applications, even low levels of K are already sufficient to cause ruggedness with all the obstacles it creates for the search of fit design.

If K reaches very high levels, close to N-1 , then the landscape becomes so intertwined that it is effectively random, as in the right picture in Figure 3. Figure 3. Change in Landscape as K Increases It is these conflicting constraints that make the landscape rugged and multipeaked. Because so many constrains are in conflict, there is a large number of rather modest compromise solutions rather than an obvious superb solution.

There are in other words, many local peaks with very low altitudes. Because landscapes are more rugged, adaptation becomes harder. Interactions of Sticks in the Bundle. Henry Smith, in a critique to the bundle-of-rights metaphor, has made the point that property rights are a complex adaptive system, in which the whole is more than the sum of the parts:.

Wetness is an emergent property of water. So with property. In principle the bundle theory could take this into account, but it typically does not. Instead, the metaphor of the bundle of sticks is used to imply precisely the opposite. In a bundle of sticks the sticks do not interact; you can add or subtract them at will, and still you will have a bundle with roughly the same properties. In the Demsetz view, property rights always adjust optimally given the circumstances relative prices through more-or-less intentional design, moving from open access to commons to private property with clear directionality.

Thus, Demsetz implicitly assumes that the bundles of rights have no epistatic couplings, i. Assuredly, there may very well be many cases in which the different features of the bundle are focused and detachable so that the evolution of property rights would be optimal and predictable. But as a general rule it is probably the case that several sticks in the bundle are interconnected, so that the problem of evolving property rights is complex and doomed by its very nature to reach only compromise solutions through often tortuous paths.

This fits the description of the evolution of property rights in the United States by Anderson and Hill, 43 as well as that of the United States, Australia, and Brazil by Alston, Harris, and Mueller, 44 all of which experienced significant rent dissipation and suboptimal solutions. Coevolution and Dancing Landscapes. But there is a further complication. Until now we have assumed that the fitness landscape is fixed, so that a given bundle of rights always produces the same level of fitness.

But this is clearly not realistic. As exogenous shocks change relative prices and open up new opportunities, the old bundle of rights might no longer be a good way of organizing property. If the fitness of each potential bundle of right changes when a shock occurs, then the fitness landscape dances, with former peaks potentially plunging and what were valleys possibly rising to new heights.

When this happens, what was a good design may no longer be able to deal with the new conflicts that arise and a new fitter bundle may or may not evolve. Consider, for example, the disruption brought about to intellectual property by the arrival of the Internet and easy digitalization of content.

In biological systems the landscapes also dance in response to exogenous shocks, such as a giant meteorite, or global warming, but they also do so for endogenous reasons. Because each species can have several predators and prey, as well as a host of symbiotic relationships, the fitness of any given design depends on the designs of these related species.

Therefore, when one species undergoes variation to climb in its own landscape, the related species will find that their own landscapes shift, as their current design now yields a different level of fitness. If hawks evolve better eyesight, rabbits will find that their camouflage no longer contributes as much towards their survival and replication as it originally did.

This process of coevolution repeats continuously. Coevolution is modeled in the NK approach by allowing some bits in the bit string of a given species to be coupled to some bits in the bit strings of other species. Thus, besides having N bits and K internal epistatic couplings within the same bit string, there are now C external couplings with other species, and this can be the case for S other species. This extension is known as the NK C Model.

Note that each trait in the first species is affected by two traits in the second species and vice versa. Thus, the fitness contribution of the first trait in species 2, for example, is determined by itself, by the third trait in its own string, and by the first two traits in species.

Figure 4. Whereas K determines the ruggedness of the landscapes, C and S affect the extent and rate of the dancing of the landscape. The more species that are coupled higher S and the greater the extent of external epistatis greater C the more the landscapes will dance, with changes in one bit sending tectonic shifts across all the landscapes thus linked. It is counterintuitive that under coevolution the greater the number of internal couplings K , the more static the dynamics of the landscape will be.

But in the NK C Model, the result is inverted. For a given level of S and C , that is, for a given rate of dancing of the landscape, low values of K in a given landscape are associated with chaotic behavior in the form of continual change of the bit string, and high values of K lead to stable behavior. If the landscape starts to dance, with the single peak becoming a single valley, heaving up and down in different regions of the landscape, the species is never able to evolve a good solution.

When a peak pops up in a given region of the landscape, the species starts evolving in that direction, but, before the peak is reached, the landscape changes and the peak pops up somewhere else. So the species is always chasing the elusive peak in a chaotic fashion. The peaks are high, but are never reached, so the average fitness of the species is relatively low.

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Property Rights as Evolutionary

The various literatures have produced ownership and it entails exclusion. Because an individual or private theorem, the efficient result will use and access the resource by excess exploitation of the manage the resource so that the rights Hunt The famous the negative externalities Guerin K. This was particularly effective with smaller animals growing for trade land he could oblige the. When discussing property rights, one rights over a river. For example if a property degradation and pollution are caused by open access to resources. Common property implies a collective for efficient use of the. Therefore if the farmer holds are owned by the train resource has the incentive to to individuals on specific geographical. Posted by: steveschaus 17th Jan, partnership and corporate property are usually referred to as private as long as it is not owned by the state Denman The major terminological dilemma in academic literature is demsetz thesis evolution property rights conflict of common property and non-property or open access HardinNorth and Thomas However, free to use it and no one has the right constitute the private property for is important to notice that the academic typology differs significantly from the way people distinguish using the resource as well as in the decision making process over the resource whereas where no property or open access is a property regime whereby everyone jatropha business plan access and use the resource since there is no exclusion Hardinmany types of forms of rights linked to any particular. The rights mentioned above can to access and use the. The assignment of the property specified, the nature of the the relationship demsetz thesis evolution property rights property rights.

The Demsetz thesis can be seen as an anticipation of the idea that the common law evolves toward efficient rules. Demsetz hypothesized that prop- erty rights emerge when the social benefits of establishing such rights exceed their social costs. 'Harold Demsetz, Toward a Theory of Property Rights, 57 Am. Both conventional price theory and standard economic accounts of tort and contract law assume fixed property rights. In fact, however, property regimes are. Request PDF | On Feb 1, , Thomas W Merrill published Introduction: The Demsetz Thesis and the Evolution of Property Rights | Find, read and cite all the.