BIOECONOMIC MODELLING OF ENDANGERED SPECIES CONSERVATION

Resource /Energy Economics and Policy,

USING LAND AS A CONTROL VARIABLE IN DENSITY-DEPENDENT BIOECONOMIC MODELS

The bioeconomic analysis of endangered species without consumptive values can be problematic when analysed with density-dependent models that assume a fixed environment size. Most bioeconomic models use harvest as a control variable, yet when modelling non-harvestable species, frequently the only variable under control of conservationists is the quantity of habitat to be made available. The authors explore the implications of this in a model developed to analyse the potential population recovery of New Zealand's yellow-eyed penguin. The penguin faces severe competition with man for the terrestrial resources required for breeding and has declined in population to perilously low levels. The model was developed to estimate the land use required for recovery and preservation of the species and to compare the results to current tourism-driven conservation efforts. It is demonstrated that land may serve as a useful control variable in bioeconomic models and that such a model may be useful for determining whether sufficient incentives exist to preserve a species. However, the model may generate less useful results for providing a specific estimate of the optimal allocation of land to such a species.Land Economics/Use,

Exploiting evolution to treat drug resistance: Combination therapy and the double bind

Although many anti cancer therapies are successful in killing a large percentage of tumour cells when initially administered, the evolutionary dynamics underpinning tumour progression mean that often resistance is an inevitable outcome, allowing for new tumour phenotypes to emerge that are unhindered by the therapy. Research in the field of ecology suggests that an evolutionary double bind could be an effective way to treat tumours. In an evolutionary double bind two therapies are used in combination such that evolving resistance to one leaves individuals more susceptible to the other. In this paper we present a general evolutionary game theory model of a double bind to study the effect that such approach would have in cancer. Furthermore we use this mathematical framework to understand recent experimental results that suggest a synergistic effect between a p53 cancer vaccine and chemotherapy. Our model recapitulates the experimental data and provides an explanation for its effectiveness based on the commensalistic relationship between the tumour phenotypes

A Safety-Case Approach for Ethical Considerations for Autonomous Vehicles

Ethical considerations for autonomous vehicles (AVs) go beyond the “trolley problem” to include such aspects as risk / benefit trade-offs, informed consent, risk responsibility and risk mitigation within a system of systems. In this paper we present a methodology for arguing that the behaviour of a given AV meets desired ethical characteristics. We identify some of the ethical imperatives surrounding the introduction of AVs and consider how decisions made during development can impact the ethics of the AV’s behaviour

Ethics and the safety of autonomous systems

The ethical landscape surrounding the introduction of autonomous vehicles is complex, and there are real concerns over whether the operational safety of these systems can be adequately demonstrated. In this paper we focus on the ethical factors relevant to the design and safety justification of autonomous systems, considering issues such as risk transfer, ALARP considerations, capability vs risk trade-offs and emergent behaviours. We look beyond the "trolley problem” to consider how design decisions can reflect a wider ethical framework. We also look at the wider landscape around the emergence of autonomous systems, with a particular focus on the driving social factors which encourage early adoption of new technologies in this domain. We present some arguments for encouraging an explicit discussion of social and ethical factors within the safety framework for autonomous systems

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

A new approach to testing multi-UAV conflict resolution algorithms is presented. The problem is formulated as a multi-objective search problem with two objectives: finding air traffic encounters that 1) are able to reveal faults in conflict resolution algorithms and 2) are likely to happen in the real world. The method uses agent-based simulation and multi-objective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, a genetic algorithm approach is used. The method is applied to test ORCA-3D, a widely cited open-source multi-UAV conflict resolution algorithm, and the method’s performance is compared with a plausible random testing approach. The results show that the method can find the required encounters more efficiently than the random search. The identified safety incidents are then the starting points for understanding limitations of the conflict resolution algorithm