The current research sought to clarify the diverging relationships between counterfactual thinking and hindsight bias observed in the literature thus far. In a non-legal context, Roese and Olson (1996) found a positive relationship between counterfactuals and hindsight bias, such that counterfactual mutations that undid the outcome also increased participants’ ratings of the outcome’s a priori likelihood. Further, they determined that this relationship is mediated by causal attributions about the counterfactually mutated antecedent event. Conversely, in the context of a civil lawsuit, Robbennolt and Sobus (1997) found that the relationship between counterfactual thinking and hindsight bias is negative. The current research sought to resolve the conflicting findings in the literature within a legal context. In Experiment One, the manipulation of the normality of the defendant’s target behavior, designed to manipulate participants’ counterfactual thoughts about said behavior, did moderate the hindsight effect of outcome knowledge on mock jurors’ judgments of the foreseeability of that outcome as well as their negligence verdicts. Although I predicted that counterfactual thinking would increase, or exacerbate, the hindsight bias, as found by Roese and Olson (1996), my results provided some support for Robbenolt and Sobus’s (1997) finding that counterfactual thinking decreases the hindsight bias. Behavior normality did not moderate the hindsight effect of outcome knowledge in Experiment Two, nor did causal proximity in Experiment Three. Additionally, my hypothesis that self-referencing may be an effective hindsight debiasing technique received little support across the three experiments. Although both the self-referencing instructions and self-report measure consistently decreased mock jurors’ likelihood of finding the defendant negligent, and self-referencing instructions decreased their foreseeability ratings in studies two and three, the self-referencing manipulation did not interact with outcome knowledge to moderate a hindsight bias effect on either foreseeability or negligence judgments. The consistent pattern of results across the three experiments, however, suggests that self-referencing may be an effective technique in reducing the likelihood of negligence verdicts.

Abstract This dissertation suggests that historical presentations should be understood as literary worlds. It studies how they are engaged with in the current media environment. The concept of a world offers a novel way of analysing how presentations are identified as being specifically historical presentations. In the case of the traditional written history, the identification of historical worlds is determined by contrasting their implied world-state at the present moment against the actual world’s world-state at the present moment with the help of counterfactual heuristics. This is done because the evidence of any e.g. past events can only be evaluated in the present as the past itself is inaccessible. The dissertation approaches the evaluation of history from a presentist point of view. It is argued that within the digital media environment, historical presentations are intuitively interpreted as incomplete presentations or as fragments of a larger whole. The functioning of historical presentations is examined as part of a large media network. History’s availability through a variety of media is then analysed through the concepts of transmedia and remediation. It is concluded that history is necessarily mediated and that the current media environment is changing how history is being engaged with. / Tiivistelmä Väitöskirja ehdottaa, että historiallisia esityksiä tulisi käsitellä kirjallisina maailmoina. Samalla tarkastellaan, kuinka historiallisia esityksiä kohdataan nykyisessä digitaalisessa mediaympäristössä. Maailmojen tarjoama käsitteellinen viitekehys antaa mahdollisuuden analysoida, kuinka historialliset esitykset tunnistetaan juuri historiallisiksi (eikä joksikin muuksi). Perinteisen kirjoitetussa muodossa esiintyvän historian tunnistamisen analyysin kohdalla esitetään, että historiallisten maailmojen implikoidun nykyhetken maailmantilaa verrataan kontrafaktuaalisen heuristiikan avulla aktuaalisen maailman nykyhetken maailmantilaan. Historiallisten väitteiden tueksi esitettävää todistusaineistoa on mahdollista arvioida vain nykyhetkessä, koska meillä ei ole pääsyä itse menneisyyteen. Näin ollen historiallisia maailmoja ei voida verrata suoraan aktuaaliseen menneisyyteen. Väitöskirja lähestyykin siis historian arviointia presentistisestä näkökulmasta. Väitöskirjassa esitetään myös näkemys, jonka mukaan nykyinen mediaympäristömme toimii siten, että se ohjaa meitä intuitiivisesti käsittelemään kaikkia historiallisia esityksiä epätäydellisinä sirpaleina, jotka näyttävät vain välähdyksen suuremmasta kokonaisuudesta. Historialliset esitykset nähdäänkin analyysissa osana laajempaa verkostoitunutta mediaa. Tämä laajennetaan transmedia-analyysiksi siitä, kuinka historiaa kohdataan ja levitetään populaarissa mediassa. Tämän pohjalta esitetään näkemys, jonka mukaan historia on välttämättä välittynyttä ja nykyinen mediaympäristö on muuttamassa suhdettamme historiallisiin esityksiin.

The aim of explainable machine learning is to aid humans in understanding how exactly complex machine learning models work. Machine learning models have offered great performance in various areas. However, the mechanisms behind how the model works and how decisions are being made remain unknown. This specific constraint increases the user’s hesitation to trust the results of the model and even to improve their performance further. Counterfactual explanation is one method to offer explainability in machine learning by indicating what would have happened if the input of a model was modified in a specific way. Fraud is the action of acquiring something from someone else in a dishonest manner. Companies’ and organizations’ vulnerability to malicious actions has been increasing due to the development of digitalization. Machine learning applications have been successfully put in place to tackle fraudulent actions. However, the severity of the impact of fraudulent actions has highlighted the need for further scientific exploration of the topic. The current research will attempt to do so by studying counterfactual explanations related to fraud detection. Latent-CF is a method for counterfactual generation that utilizes an autoencoder and gradient descent in its latent space. LatentCF++ is an extension of Latent-CF. It utilizes a classifier and an autoencoder. The aim is to perturb the encoded latent representation through a gradient descent optimization for counterfactual generation so that the initially undesired class is then classified with the desired prediction. Compared to Latent-CF, LatentCF++ uses Adam optimization and adds further constraints to ensure that the generated counterfactual’s class probability surpasses the set decision boundary. The research question the current thesis addresses is: “To what extent can LatentCF++ provide reliable counterfactual explanations in fraud detection?”. In order to provide an answer to this question, the study is applying an experiment to implement a new application of LatentCF++. The current experiment utilizes a onedimensional convolutional neural network as a classifier and a deep autoencoder for counterfactual generation in fraud data. This study reports satisfying results regarding counterfactual explanation production of LatentCF++ on fraud detection. The classification is quite accurate, while the reconstruction loss of the deep autoencoder employed is very low. The validity of the counterfactual examples produced is lower than the original study while the proximity is lower. Compared to baseline models, k-nearest neighbors outperform LatentCF++ in terms of validity and Feature Gradient Descent in terms of proximity.

Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. The project proposes designing and implementing deep neural networks, tailored explicitly for time series multivariate data from sensors incorporated in vehicles, to effectively capture intricate temporal dependencies and interactions among variables. As this project is conducted in collaboration with Scania, Sweden, the models are trained on datasets encompassing various vehicle sensor data. Different deep learning architectures, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), are explored and compared to identify the most suitable model for anomaly detection tasks for the specified time series data and CNN found to perform well for the data used in the study. Furthermore, interpretability techniques are incorporated into the developed models to enhance their transparency and provide insights into the reasons behind detected anomalies. Interpretability is crucial in real-world applications to facilitate trust, understanding, and decision-making. Both model-agnostic and model-specific interpretability methods were employed to highlight the relevant features and contribute to the interpretability of the anomaly detection models. The performance of the proposed models is evaluated using test datasets with anomaly data, and comparisons are made against existing anomaly detection methods to demonstrate their effectiveness. Evaluation metrics such as precision, recall, false positive rate, F1 score, and composite F1 score are employed to assess the anomaly detection models' detection accuracy and robustness. For evaluating the interpretability method, Kolmogorov-Smirnov Test is used on counterfactual examples. The outcomes of this research project will contribute to developing advanced anomaly detection techniques that can effectively analyse time series multivariate data collected from sensors incorporated in vehicles. Incorporating interpretability techniques will provide valuable insights into the detected anomalies, enabling better decision-making and improved trust in the deployed models. These advancements can potentially enhance anomaly detection systems across various domains, leading to more reliable and secure operations.

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Today more and more algorithms and their applications are entering into the everyday life of each of us. Algorithms can help people to make more effective choices through historical data analysis, generating predictions to present to the user in the form of advice and suggestions. Given the increasing popularity of these suggestions, a greater understanding of how people could increase their judgment through the suggestions presented is needed, in order to improve the interface design of these applications. Since the envision of Artificial Intelligence (AI), technical progress has the intent of surpassing human performance and abilities (Crandall et al., 2018). Less consideration has been given to improve cooperative relationships between human agents and computer agents during decision tasks. No study up to date has investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system. According to Zeelenberg et al. (Martinez & Zeelenberg, 2015; Martinez, Zeelenberg, & Rijsman, 2011a; Zeelenberg & Pieters, 1999), there are two emotions strongly related to wrong decisions, regret, and disappointment. The objective of this research is to understand the different effects of disappointment and regret on participants’ behavioral responses to failed suggestions given by algorithm-based systems. The research investigates how people deal with a computer suggestion that brings to a not satisfying result, compared to a human suggestion. To achieve this purpose, three different scenarios were tested in three different experiments. In the first experiment, the comparison was amongst two wrong suggestions in a between-subjects design through the presentation of a flight ticket scenario with two tasks. The first study analyzed exploratory models that explain the involvement of the source of suggestion and the trust in the systems in the experience of counterfactual emotions and responsibility attribution. The second experiment takes advantage of a typical purchase scenario, already used in the psychological literature, which had the aim to solve the issues found in the first study and test the algorithm aversion paradigm through the lenses of a classic study of regret literature. Results showed that, contrary to early predictions, people blame more the source of the suggestion when it comes from a human as compared with an intelligent computer suggestion. The third study had the aim to understand the role of counterfactuals through a paradigmatic experiment from algorithm aversion literature. In this study, the main finding is about the reliance people have on the algorithmic suggestion, which is higher compared to the reliance they have with a human suggestion. Nevertheless, people felt more guilt when they had a wrong outcome with a computer compared with a suggestion given by a person. Results are relevant in order to better understand how people decide and trust algorithm-based systems after a wrong outcome. This thesis is the first attempt to understand this algorithm aversion from the experienced counterfactual emotions and their different behavioral consequences. However, some of these findings showed contradictory results in the three experiments; this could be due to the different scenarios and participants’ thoughts and perceptions of artificial intelligence-based systems. From this work, three suggestions can be inferred to help designers of intelligent systems. The first regards the effective involvement of counterfactuals during the user interaction with a wrong outcome and the potential behavioral consequences that could affect the future use of the intelligent system. The second suggestion is the contribution to the importance of the context in which decisions are made, and the third guideline suggests the designer rethink about anthropomorphism as the best practice to present suggestions in the occurrence of potential wrong outcomes. Future works will investigate, in a more detailed way the perceptions of users and test different scenarios and decision domains.

2017 has been described as a decisive year for the Republic of Moldova. Following years of economic and political turmoil, it is more urgent than ever before that crucial reforms are not only adopted, but fully implemented – primarily within justice, media and banking sectors. Using a theory of impartial institutions and two central theories of state intervention into media markets, this counterfactual deductive thesis sets out to investigate arguments for and against a system of state subsidies to private media as a tool to increase Quality of Government in Moldova. Through analyses of qualitative interviews with six country experts within relevant fields as well as secondary data, the conclusion of this single case study is primarily confirming previous research indicating on the one hand, that a more social responsible role of the state within Eastern European media markets is a realistic future path, on the other hand that it can hardly be expected soon. Further, the possibility of media to improve Quality of Government is perceived as low – even with sufficient financial resources – due to lack of other prerequisites such as accessibility, accountability and responsiveness; as well as low scores on crucial indicators such as corruption, law and order and quality of bureaucracy. Just like democratic institutions can be destabilizing under wrong circumstances, state subsidies given to wrong beneficiaries within a media market, could undermine democratic legitimacy and accountability. Both findings of previous research – related to state governance on the one hand and media governance on the other – are perceived as particularly relevant in this study of Moldova. However, there have been promising signs lately on economic structural reforms improving the conditions for civil society and media in Moldova, indicating for reevaluating the potential of state support in the future. Further, I recommend greater emphasis put on the distinction between political and non-political owners when examining the effects of media ownership concentration and its effects on governments and societies at large.

No description available.

<p>There is longstanding interpretive dispute between commentators over Spinoza’s commitment to <i>necessitarianism</i>, the doctrine that all things are metaphysically necessary and none are contingent. Those who affirm Spinoza’s commitment to the doctrine adhere to <i>the necessitarian interpretation</i> whereas those who deny it adhere to what I call <i>the semi-necessitarian interpretation</i>. As things stand, the disagreement between commentators appears to have reached an impasse. Notwithstanding, there seems to be no disagreement among commentators on the question of necessitarianism’s philosophical plausibility as a metaphysical view: the doctrine is wildly untenable. This consensus view is more relevant to the interpretive debate than few have recognized, since leading semi-necessitarian commentators take the doctrine’s alleged absurdity to be one of the most compelling reasons (if not <i>the</i> most compelling reason) to prefer their reading over the necessitarian interpretation: for, as a matter of methodological principle, great philosophers like Spinoza should not be ascribed ridiculous views in the absence of better evidence. </p> <p>This dissertation seeks to defend Spinoza’s commitment to necessitarianism on both the interpretive and philosophical fronts. I argue not only that the necessitarian interpretation of Spinoza is more plausible than the semi-necessitarian interpretation on textual grounds, but that Spinoza’s necessitarianism is a serviceable philosophical view whose tenability has been almost entirely overlooked and perfunctorily rejected. The principal basis upon which I build this defense is Spinoza’s rich and fascinating view of essences—what I simply refer to as his <i>essentialism</i>. Spinoza’s essentialism forms the bedrock of his metaphysics and is significant not least because it underlies and informs doctrines like his necessitarianism. Spinoza’s essentialism supplies resources to answer not just interpretive problems associated with necessitarianism, but philosophical challenges to the plausibility of the doctrine. My defense of Spinoza’s necessitarianism on philosophical grounds also offers a novel way of getting past much of the current interpretive impasse among commentators by effectively undercutting the methodological motivation for the semi-necessitarian reading. In addition to my defense on the interpretive front, then, my defense on the philosophical front provides supplementary reason to <i>a fortiori</i> favor the necessitarian reading of Spinoza.</p>

This thesis aims to examine the economic effect in terms of real GDP at constant 2017 national prices (in mil. 2017 US$) of being a member in the European Union in the case of Sweden. The method used to answer the research question is mainly by the Synthetic Control Method. Difference-in-difference estimations were also conducted as a complement to compare the results between the two methods. Previous literature in the subject has for the most part received results indicating that a membership in the European Union contributes to an increase in certain economic growth aspects for the member countries. The economic theories about common markets points at that a common market can be both advantageous but also disadvantageous depending on the setting. Theories more closely related to the European Union present rather ambiguous answers to certain economic effects of a membership but in some cases, it seems to be possible to assume some positive economic effect from being a European Union-member. The results in this paper seem to be robust in the way that the effect on the real GDP at constant 2017 national prices (in mil. 2017 US$) in Sweden from its membership in the European Union is negative in all estimations that were made. However, none of the results were statistically significant so there is not enough evidence to say that the Swedish real GDP at constant 2017 national prices (in mil. 2017 US$) would have been different to the factual outcome if Sweden did not join the European Union in 1995.