The problems of controlling exotic species have been acknowledged as serious threats to an indigenous ecosystem as well as to society. In response to these threats, various management programs of exotic species have b...
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The problems of controlling exotic species have been acknowledged as serious threats to an indigenous ecosystem as well as to society. In response to these threats, various management programs of exotic species have been proposed for supporting eradication in many regions. Although eradication is regarded as the first-best solution, such attempts have been unsuccessful in reality due to mainly two factors: (1) stock-dependent catchability, and (2) uncertainties. This article demonstrates when to aim at eradication through addressing an optimal adaptive management strategy in the framework of a bio-economic model with the aforementioned factors. The study sets out that the sensitivity of catchability in response to a change in the existing stock determines whether or not aiming at eradication is justified. The results also show that process uncertainty associated with stock growth significantly affects the timing of removal actions for eradication, and an increase in the degree of uncertainty could help achieve eradication in a cost-effective manner if we optimally adapt our removal actions to the uncertainty.
We characterize the optimal harvest of a renewable resource in a generalized stochastic spatially explicit model. Despite the complexity of the model, we are able to obtain sharp analytical results. We find that the o...
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We characterize the optimal harvest of a renewable resource in a generalized stochastic spatially explicit model. Despite the complexity of the model, we are able to obtain sharp analytical results. We find that the optimal harvest rule in general depends upon dispersal patterns of the resource across space, and only in special circumstances do we find a modified golden rule of growth that is independent of dispersal patterns. We also find that the optimal harvest rule may include closure of some areas to harvest, either on a temporary or permanent basis (biological reserves). Reserves alone cannot correct open access, but may, under sufficient spatial heterogeneity and connectivity, increase profits if appropriate harvest controls are in place outside of reserves. (C) 2008 Elsevier Inc. All rights reserved.
Flexible conservation management, where measures (e.g., mowing of meadows, removing invasive species) are selected in each decision period depending on the current state of the ecological system, is generally perceive...
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Flexible conservation management, where measures (e.g., mowing of meadows, removing invasive species) are selected in each decision period depending on the current state of the ecological system, is generally perceived as superior to fixed management, where the same measure is applied in each decision period independent of the current state of the system. In past comparisons of fixed and flexible conservation strategies the additional costs that arise only in flexible strategies have usually been ignored. In this paper, we present a framework to integrate costs of flexible management into the evaluation of flexible conservation strategies. Using the example of an endangered butterfly species we demonstrate that the costs of flexible management may reverse the rank order of flexible and fixed conservation strategies, such that fixed strategies may lead to better ecological results than flexible ones for the same financial budget.
I describe my personal evolution as a modeller of behaviour, both human and (non-human) animal behaviour, using dynamic state-variable models. At first I worked in renewable resource Economics, especially the economic...
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I describe my personal evolution as a modeller of behaviour, both human and (non-human) animal behaviour, using dynamic state-variable models. At first I worked in renewable resource Economics, especially the economics of marine fisheries where I collaborated extensively with Gordon R. Munro. Subsequently, in collaboration with Marc Mangel (and many field biologists) I worked in Behavioural Ecology. Mathematical models have played a major role in both of these subjects, but until recently mostly static models were used, on the grounds that dynamic (not to mention stochastic) models were too difficult to work with. I express the hope that our use of relatively simple (but not too simple) dynamic models has established the fact that such models can be extremely helpful, perhaps essential, in understanding many aspects of behaviour.
In this paper, we show how ordering time can be used as a mechanism to mitigate the supply risks of the unreliable newsboy. We consider the situation where a firm has an option of placing a series of sequential orders...
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In this paper, we show how ordering time can be used as a mechanism to mitigate the supply risks of the unreliable newsboy. We consider the situation where a firm has an option of placing a series of sequential orders such that a new order is placed only after the yield from the previous order is known and derive an optimal order quantity for each ordering stage. The effectiveness of our approach is assessed by comparing its expected cost with the expected cost of the conventional reliable newsboy problem. It is demonstrated that the adverse effect of supply side risks can be almost completely negated by adopting our approach when the combined purchasing and holding costs for a low cost supplier at an earlier stage are equal to those of a high cost supplier at the next stage. Computational experiments suggest that the sequential ordering policy performs better than a simultaneous ordering strategy when customer service level norms become stringent and inventory holding costs decrease.
Past research found agricultural producers' conditional responses during the growing season are important adaptations to weather and other stochastic events. Failing to recognize these responses overstates the ris...
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Past research found agricultural producers' conditional responses during the growing season are important adaptations to weather and other stochastic events. Failing to recognize these responses overstates the risks confronting producers and understates their ability to respond to adverse circumstances. dynamicprogramming (DP) provides a means for determining optimal long-term crop management plans. However, most applications in the literature base their analysis on annual time steps with fixed strategies within the year, effectively ignoring conditional responses during the year. We suggest an alternative approach that captures the strategic responses within a cropping season to random weather variables as they unfold, reflecting farmers' ability to adapt to weather realizations. We illustrate our approach by applying it to a typical cereal farm in Karak, Jordan. The results show that including conditional within-year responses to weather reduces the frequency of fallowing by 23% and increases expected income by 9%.
We consider an original equipment manufacturer (OEM) who has outsourced the production activities to a contract manufacturer (CM). The CM produces for multiple OEMs on the same capacitated production line. The CM requ...
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We consider an original equipment manufacturer (OEM) who has outsourced the production activities to a contract manufacturer (CM). The CM produces for multiple OEMs on the same capacitated production line. The CM requires that all OEMs reserve capacity slots before ordering and responds to these reservations by acceptance or partial rejection, based on allocation rules that are unknown to the OEM. Therefore, the allocated capacity for the OEM is not known in advance, also because the OEM has no information about the reservations of the other OEMs. Based on a real-life situation, we study this problem from the OEM's perspective who faces stochastic demand and stochastic capacity allocation from the contract manufacturer. We model this problem as a single-item, periodic review inventory system, and we assume linear inventory holding, backorder, and reservation costs. We develop a stochastic dynamic programming model, and we characterize the optimal policy. We conduct a numerical study where we also consider the case that the capacity allocation is dependent on the demand distribution. The results show that the optimal reservation policy is little sensitive to the uncertainty of capacity allocation. In that case, the optimal reservation quantities hardly increase, but the optimal policy suggests increasing the utilization of the allocated capacity. Further, in comparison with a static policy, we show that a dynamic reservation policy is particularly useful when backorder cost and uncertainty are low. Moreover, we show that for the contract manufacturer, to achieve the desired behavior, charging little reservation costs is sufficient.
We study the optimal corporate policy of a risk-averse shareholder under leverage-dependent borrowing costs and other financial frictions. The firm's objective is to maximize the risk-adjusted shareholder value by...
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We study the optimal corporate policy of a risk-averse shareholder under leverage-dependent borrowing costs and other financial frictions. The firm's objective is to maximize the risk-adjusted shareholder value by co-optimizing investment, dividend, and debt policies considering endogenous (leverage-dependent) leveraging costs, tax shield, as well as costs of equity issuance and asset fire sale. The resulting multistage stochastic linear program model is efficiently solved by the stochastic Dual dynamicprogramming algorithm. After certifying that the risk-neutral results are consistent with previous studies, our model helps to resolve the well-known low-leverage puzzle, a dissonance between the empirical and structural modeling literature in corporate finance. Our case study results show that risk-aversion combined with leverage-dependent borrowing cost can significantly reduce the optimal leverage ratio as well as the firm size without significantly compromising dividend payments.
作者:
Peterson, James T.Duarte, AdamOregon State Univ
Dept Fisheries & Wildlife Oregon Cooperat Fish & Wildlife Res Unit US Geol Survey 104 Nash Hall Corvallis OR 97331 USA Oregon State Univ
Dept Fisheries & Wildlife Oregon Cooperat Fish & Wildlife Res Unit 104 Nash Hall Corvallis OR 97331 USA
Considerable resources have been invested in ecological restoration projects across the globe to restore ecosystem integrity. Restoration strategies are often diverse and have been met with mixed success. In this arti...
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Considerable resources have been invested in ecological restoration projects across the globe to restore ecosystem integrity. Restoration strategies are often diverse and have been met with mixed success. In this article, we describe the Chinook salmon (Oncorhynchus tshawytscha) decision-support models developed by the Central Valley Project Improvement Act Science Integration Team as part of a larger structured decision-making effort aimed at maximizing natural adult production of Chinook salmon in California's Central Valley, the United States. We then describe the decision-analytic tools the stakeholder group used to solve the models and explore model results, including stochastic dynamic programming, forward simulation, proportional scoring, relative loss, expected value of perfect information, response profile analyses, and indifference curves. Using these tools, the stakeholder group was able to develop and evaluate restoration strategies for multiple Chinook salmon runs simultaneously, a first for the restoration program. We found that actions targeted at one run were detrimental to others, which was unexpected. Furthermore, information uncovered during this process was used to direct efforts towards targeted research/monitoring to reduce critical uncertainties in salmon demographic rates and make better restoration decisions moving forward. The decision sciences have established a wide range of analytical tools and approaches to simplify complex problems into key components, and we believe the concepts described in this article are of great interest and can be applied by many restoration practitioners that undoubtedly face similar difficulties when implementing restoration strategies for complex systems.
Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate t...
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Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate techniques. Behavioural and life history aspects can be implemented in IBMs through adaptive models based on genetic algorithms and neural networks (individual-based-neural network-genetic algorithm, ING). To investigate the precision of the adaptation process, we present three cases where solutions can be found by optimisation. These cases include a state-dependent patch selection problem, a simple game between predators and prey, and a more complex vertical migration scenario for a planktivorous fish. In all cases, the optimal solution is calculated and compared with the solution achieved using ING. The results show that the ING method finds optimal or close to optimal solutions for the problems presented. In addition it has a wider range of potential application areas than conventional techniques in behavioural modelling. Especially the method is well suited for complex problems where other methods fail to provide answers.
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