Three Essays on Incentive Design

We present three distinct works on the subject of incentive design. The first focuses on a fundamental aspect of all principal-agent models, the participation constraint. We endogenise the constraint, allowing the agent to influence his outside option, albeit at some detriment to the project he is contracted to work upon. We compare the optimal contract to the literature on the supposed trade-off between risk and incentives. We find support for the Prendergast (2002) observation of a positive relationship between the two variables and ofer an explanation through the use of said influence activities. The second contribution introduces another principal-agent framework for models with both adverse selection and moral hazard, with the novel inclusion of limited liability. Described in a target-setting environment, the findings are related to and support the use of tenure contracts in academia. This is justified by the fact that pooling equilibria maximise the value to the principal and fully separating equilibria are implemented with non-monotonic wage structures. Finally, in opposition to conventional literature, those of low type make rent gains over and above their reservation utility, while the high types break even. The final chapter studies organisational design and allocation of control. We offer conditions whereby firms would wish to integrate, or profit-share, with another, given varying degrees of control allocation. We show that integration comes at a lower cost for the decision-making firm when control is contractible as opposed to transferable. Also we show that the level of incompatibility between firms, unrelated to financial gain, can affect the integration decision.ESR

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates

Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real world problems. We propose a simple mechanism for building small heterogeneous ensembles based on exponentially weighting the probability estimates of the base classifiers with an estimate of the accuracy formed through cross-validation on the train data. We demonstrate through extensive experimentation that, given the same small set of base classifiers, this method has measurable benefits over commonly used alternative weighting, selection or meta classifier approaches to heterogeneous ensembles. We also show how an ensemble of five well known, fast classifiers can produce an ensemble that is not significantly worse than large homogeneous ensembles and tuned individual classifiers on datasets from the UCI archive. We provide evidence that the performance of the Cross-validation Accuracy Weighted Probabilistic Ensemble (CAWPE) generalises to a completely separate set of datasets, the UCR time series classification archive, and we also demonstrate that our ensemble technique can significantly improve the state-of-the-art classifier for this problem domain. We investigate the performance in more detail, and find that the improvement is most marked in problems with smaller train sets. We perform a sensitivity analysis and an ablation study to demonstrate the robustness of the ensemble and the significant contribution of each design element of the classifier. We conclude that it is, on average, better to ensemble strong classifiers with a weighting scheme rather than perform extensive tuning and that CAWPE is a sensible starting point for combining classifiers

Heterogeneous ensembles and time series classification techniques for the non-invasive authentication of spirits

Spirits are a prime target for fraudulent activity. Particular brands, production processes, and other factors such as age can carry high value, and leave space for mimicry. Further, the improper production of spirits, either maliciously or through negligence, can result in harmful substances being sold for consumption. Lastly, genuine spirits producers themselves must ensure the quality and standardisation of their products before sale. Authenticating spirits can be a time consuming and destructive process, requiring sealed bottles to be opened for access to the product. It is therefore desirable to have a fast, non-invasive means of indicating the authenticity, safety, and correctness of spirits. We advance and prototype such a system based on near infrared spectroscopy, and generate datasets for the detection of correct alcohol concentrations in synthesised spirits, for the presence of methanol in genuine spirits, and for the distinction of particular genuine products in a given bottle. The standard chemometric pipelines for the analysis of spectra involve smoothing of the signal, standardising for global intensity, possible dimensionality reduction, and some form of least squares regression. This has decades of proof behind it, and works under the assumptions of clean signal gathering, potentially the separation of sample and particular substance of interest, and the generally linear relationship of light received/blocked and the analyte’s contents. In the proposed system, at least one of these assumptions must be violated. We therefore investigate the use of modern classification techniques to overcome these challenges. In particular, we investigate and develop ensemble methods and time series classification algorithms. Our first hypothesis is that algorithms which consider the ordered nature of the wavelength features, as opposed to treating the spectra effectively as tabular data, can better handle the structural changes brought about by different bottle and environmental characteristics. The second is that ensembling heterogeneous classifiers is the best initial technique for a new data science problem, but should in particular be helpful for the spirit authentication problem, where different classifiers may be able to correct for different defects in the data. In initial investigations on datasets of synthesised alcohol solutions and different products, we prove the feasibility of the authentication system to make at least indicative predictions of authenticity, but find that it lacks the precision and accuracy needed for anything more than indicative results. Following this, we propose a novel heterogeneous ensembling scheme, CAWPE, and perform a large scale evaluation on public archives to prove its efficacy. We then outline improvements in the time series classification space that lead to the state of the art meta-ensemble HIVECOTE 2.0, which makes use of CAWPE. We lastly apply the developed techniques to a final dataset on methanol concentration detection. We find that the proposed system can classify methanol concentration in arbitrary spirits and bottles from ten possible values, containing as little as 0.25%, to an accuracy of 0.921. We further conclude that while heterogeneously ensembling tabular classifiers does improve the authentication of spirits from spectra, time series classification methods confer no particular advantage beyond tabular methods