A coevolution model of defensive medicine, litigation and medical malpractice insurance

 

We model the interactions between physicians and patients, subject to clinical and legal risks, by means of an evolutionary game. In each instant of time, there are a large number of random pairwise encounters between members of the two populations. In each encounter, a physician heals a patient. The outcome of the healing process is uncertain and may result in patient harm; if that happens, the patient may sue the physician for medical malpractice. Physicians have to choose between two alternative treatments, with different levels of benefits and risks. The safer treatment is also the less effective; therefore its provision corresponds to practicing "negative" defensive medicine.

Physicians prevent, at least partially, negligence charges by buying medical malpractice insurance. This transfers the risk of litigation from the physician to the insurer.
The dynamics we analyze are determined by a three-dimensional discrete-time dynamic system, where the variables x and y are, respectively, the shares of defensive physicians and litigious patients, while the variable a represents the insurance premium.
In such a context, we study the role played by model's parameters related to the accuracy of the judicial system and legal reforms in shaping the coevolution between healthcare providers' and patients' choices and price dynamics of medical malpractice insurance.

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Carbon leakage in 3D: on the dynamics of green, dirty and relocating firms under the ETS

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The debate on the carbon leakage risk of unilateral climate policies is gaining momentum along with the increase in carbon prices and in the ambition of emissions reduction targets. While empirical evidence on carbon leakage is weak or absent so far, more firms might decide to delocalize their activity in the future due to higher carbon prices. To investigate this issue, we propose a simple theoretical model which analyses the choices of a population of firms subject to an Emissions Trading System (ETS). Each firm has three alternative strategies at disposal: (i) "green": stop polluting by investing in a clean technology, (ii) "dirty": keep polluting by purchasing the correspondent emission allowances under the ETS, (iii) "relocating": keep polluting by relocating its activities to an ETS-free jurisdiction.

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When humans play evolutionary games with animal species

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Taking into account of a rapidly growing literature studying the negative effects of changes in animal behaviour induced by human activities on ecological dynamics, biodiversity preservation, and human welfare, we model the interaction between a population of humans and a population of specimens of a species by means of an evolutionary game. In such a game, we assume that humans can adopt two alternative behaviours (strategies), one with a high environmental impact (HI) and the other with a low impact (LI). The specimens of the species also have two alternative behaviours: they can behave in a typical (T) or non-typical (NT) way. The former corresponds
to the natural strategy hanimals would typically adopt in the absence of human interference (e.g. hunting prey) while the latter can be seen as an adaptive behavior adopted by the specimens to defend themselves from the negative consequences of human action. The analysis of our model shows that the adoption process of the strategies HI, LI, NT, T can generate a great variety of dynamic regimes, even if the ecological context we consider is very simple.