Past Choices Research Articles

From Bail-out to Bail-in: explaining the variegated responses to the international financial aid requests of Ireland and Cyprus

ABSTRACT The financial crisis that hit the Eurozone in 2010 drove a number of its members request assistance from their EU partners. The reaction to these requests at the European level came in coordination with the IMF, but not necessarily in a uniform manner. This article compares the adjustment programmes of Ireland and Cyprus. Despite their similarity on the independent variable (local economic conditions), the design of the Irish and Cypriot programmes (the dependent variable) differed fundamentally, not least because of the introduction of the bail-in clause in Cyprus, involving direct loses for depositors. This article seeks to explain how this key policy departure came to be and what were the conditions that made its introduction possible by examining the temporal evolution of the crisis response and focusing in particular on four explanatory themes: (i) time as a negotiating (dis)advantage; (ii) time as a platform for policy learning; (iii) time as a justifier of past policy choices (path dependency) and (iv) time as a signifier of future policy choices (path setting).

Testing the representativity of Palaeolithic site distribution: The role of sampling bias in the european upper and Final Palaeolithic record

Archaeological sites are not distributed evenly throughout the landscape. For the Palaeolithic record, signals derived from the inhomogeneous spatial patterns are used to infer spatial decision-making processes or ecological preferences of our ancestors. However, to date it is still largely unclear how sampling biases affect the large-scale distribution of sites and whether the observable spatial patterns are actually representative of the distribution of humans in the palaeo-landscape. To answer this question, this study assesses the spatial distribution of 4200 Upper and Final Palaeolithic occupations from two different perspectives, i.e., past settlement choice and likelihood of discovery. On the one hand, site distribution is thus examined for settlement-relevant factors such as topography, geology and sedimentology. On the other, discovery-relevant biases, such as recent land cover and building activity are analysed. The comprehensive spatial and statistical assessments show that the actual distribution of sites seems to be most strongly influenced by sampling biases. The assessed environmental variables representing the settlement factors show a far lower statistical association to the distribution of sites. They do, however, still support several common archaeological assumptions. For all approaches using site distribution as input, such as predictive modelling, the results of this study suggests that the sampling bias must be addressed. To this end, we suggest including environmental variables addressing discovery-relevant factors to quantify the potential biases. For further studies on the sampling bias of Upper and Final Palaeolithic sites, we recommend building on this pilot study by adding more occupations to the dataset and more environmental variables to the settlement and discovery factors. Due to the current positive trend in openly available geo-datasets, we see great future potential in this.

Positively correlated choice

We provide an axiomatic foundation for a choice model with two periods between which preferences are updated, but the second period choices are positively correlated with past choices in a manner that is unrelated to the agent’s preferences. Specifically, in our model, the agent chooses alternative x over alternative y in contrast to his past choice if and only if the difference between the utility of x and that of y is higher than some fixed cost. While restrictive in its nature, this representation captures several related but distinctive phenomena: a taste for consistency, cognitive dissonance, the escalation of commitment, passive choice, and habit formation. We also provide a representation that allows for a more general form of cost and the revealed preference implications of our models. Finally, we connect our representation to the theories of imperfect discrimination.

The philosophy of collective memory in the novel “The Buried Giant” by Kazuo Ishiguro

Memory is a major theme running through Kazuo Ishiguro's works, one of which is The Buried Giant. This study aims to analyze the concept of collective memory in Kazuo Ishiguro's novel The Buried Giant through hermeneutic interpretation and sociological analysis. The results show that this novel links collective memory with individual experience and generational identity whilst making aging a central element in the exploration of time and history. In the novel, collective memory is seen through the prism of aging. The aging characters serve as a metonymy to convey the image of memory. They find themselves in circumstances broadcasting a horrific story of decline and marginalization of the nation because they cannot access the past and move into the future. They revisit the story of their lives, but even though they can recount their losses, they do not seem to be critical of their past choices or their responsibilities in the global conflicts they lived through. In The Buried Giant, the aging characters are the ones who come to terms with their individual and collective histories to face their remaining years. This is not an idealized vision of wisdom; rather, it is an acceptance of complicity and guilt. The results can be applied in literary, sociological, and historical studies concerning the collective memory of different historical periods. They are of practical value as they contribute to the study of collective memory in literary theory. Research on collective memory in literature sheds light on the ways historical events and shared experiences impact human behavior, beliefs, and decision-making processes.

Deriving Treatment Decision Support From Dutch Electronic Health Records by Exploring the Applicability of a Precision Cohort-Based Procedure for Patients With Type 2 Diabetes Mellitus: Precision Cohort Study.

The rapidly increasing availability of medical data in electronic health records (EHRs) may contribute to the concept of learning health systems, allowing for better personalized care. Type 2 diabetes mellitus was chosen as the use case in this study. This study aims to explore the applicability of a recently developed patient similarity-based analytics approach based on EHRs as a candidate data analytical decision support tool. A previously published precision cohort analytics workflow was adapted for the Dutch primary care setting using EHR data from the Nivel Primary Care Database. The workflow consisted of extracting patient data from the Nivel Primary Care Database to retrospectively generate decision points for treatment change, training a similarity model, generating a precision cohort of the most similar patients, and analyzing treatment options. This analysis showed the treatment options that led to a better outcome for the precision cohort in terms of clinical readouts for glycemic control. Data from 11,490 registered patients diagnosed with type 2 diabetes mellitus were extracted from the database. Treatment-specific filter cohorts of patient groups were generated, and the effect of past treatment choices in these cohorts was assessed separately for glycated hemoglobin and fasting glucose as clinical outcome variables. Precision cohorts were generated for several individual patients from the filter cohorts. Treatment options and outcome analyses were technically well feasible but in general had a lack of statistical power to demonstrate statistical significance for treatment options with better outcomes. The precision cohort analytics workflow was successfully adapted for the Dutch primary care setting, proving its potential for use as a learning health system component. Although the approach proved technically well feasible, data size limitations need to be overcome before application for clinical decision support becomes realistically possible.

Estimating the Ontogenetic Age and Sex Composition of Faunal Assemblages with Bayesian Multilevel Mixture Models

Understanding the ontogenetic age and sex composition of zooarchaeological assemblages can reveal details about past human hunting and herding strategies as well as past animal morphology and behavior. As such, the accuracy of our estimates underlies our ability to ascertain details about site formation and gain insights into how people interacted with different animals in the past. Unfortunately, our estimates typically rely on only a small number of bones, limiting our ability to fruitfully use these estimates to make meaningful comparisons to theoretical expectations or even between multiple assemblages. This paper describes a method to use zooarchaeological remains with standard biometric measurements to estimate the ontogenetic age and sex composition of the assemblage, focused on immature, adult-sized female, and adult-sized male specimens. The model uses a Bayesian framework to ensure that the parameter estimates are biologically meaningful. Simulated assemblages show that the model can accurately estimate the biometry and composition of zooarchaeological assemblages. Two archaeological case studies also show how the model can be applied to produce tangible insights. The first, focused on sheep from Neolithic Pinarbaşı B, highlights the model’s ability to elucidate site formation and function. The second, focused on cattle remains from four assemblages from 7th-6th millennium BCE northwestern Anatolia, showcases how to use the mixture modeling results to compare assemblages to one another and to specific hypotheses. This modeling framework provides a new avenue for investigating long-term trajectories in animal biometry alongside contextual analyses of past human choices in butchery and consumption.

What's Your Pension Story? Women's Perspectives during the COVID-19 Pandemic on Their Old-Age Pension Status, Past and Present.

This study examines the present and retrospective views of mothers who are nearing or are at retirement age regarding their economic status, pension planning, and perceptions of state pension policy. The paper addresses gaps in the literature on the cross-intersections of employment history, vulnerable economic retirement status, and marital and parental status, thereby adopting a life course perspective. Based on in-depth interviews of thirty-one mothers (ages 59-72) during the COVID-19 pandemic, the findings revealed five themes-economic abuse: an unequal distribution of pension funds following divorce; regrets over past choices; COVID-19 and pensions; the state's responsibility for old-age economic security; and knowledge is important, and I can help others. The study concludes that the majority of women at these ages perceive their current economic situation as a product of insufficient familiarity with pension plans, while voicing opinions about the state's irresponsibility regarding people of retirement age.

What shall I learn? Two-stage decision making under social influence on corporate E-learning platforms

E-learning platforms have increasingly been adopted by corporate employees in the workplace. On these platforms, users typically follow a two-stage decision-making process: they first choose which content to learn and then decide how much to continue learning. The decisions of individual employee users are influenced by members of the same workplace organization (group influence) and general users on the platform from other organizations (mass influence). Extant research has not shown how different types of social influence impact different decisions. Using data from a corporate e-learning platform, this study examines how group influence and mass influence support employees' learning decisions, from the perspective of the elaboration likelihood model (ELM). The results reveal that mass users' past choices only influence low-elaborative choice decisions but not high-elaborative engagement decisions. In contrast, workgroup members' past choices influence both the low-elaborative choice and high-elaborative engagement decisions. Furthermore, positive synergy exists between the two types of social influence for the choice decision, but the synergy dissipates for the engagement decision. These findings can help online content platforms design appropriate information-sharing systems to influence users' choice and engagement decisions. The results can also help corporates take advantage of social influence to motivate employees to engage in work-related online learning.

Goal Choices Modify Frontotemporal Memory Representations.

Adapting flexibly to changing circumstances is guided by memory of past choices, their outcomes in similar circumstances, and a method for choosing among potential actions. The hippocampus (HPC) is needed to remember episodes, and the prefrontal cortex (PFC) helps guide memory retrieval. Single-unit activity in the HPC and PFC correlates with such cognitive functions. Previous work recorded CA1 and mPFC activity as male rats performed a spatial reversal task in a plus maze that requires both structures, found that PFC activity helps reactivate HPC representations of pending goal choices but did not describe frontotemporal interactions after choices. We describe these interactions after choices here. CA1 activity tracked both current goal location and the past starting location of single trials; PFC activity tracked current goal location better than past start location. CA1 and PFC reciprocally modulated representations of each other both before and after goal choices. After choices, CA1 activity predicted changes in PFC activity in subsequent trials, and the magnitude of this prediction correlated with faster learning. In contrast, PFC start arm activity more strongly modulated CA1 activity after choices correlated with slower learning. Together, the results suggest post-choice HPC activity conveys retrospective signals to the PFC, which combines different paths to common goals into rules. In subsequent trials, prechoice mPFC activity modulates prospective CA1 signals informing goal selection.SIGNIFICANCE STATEMENT HPC and PFC activity supports cognitive flexibility in changing circumstances. HPC signals represent behavioral episodes that link the start, choice, and goal of paths. PFC signals represent rules that guide goal-directed actions. Although prior studies described HPC-PFC interactions preceding decisions in the plus maze, post-decision interactions were not investigated. Here, we show post-choice HPC and PFC activity distinguished the start and goal of paths, and CA1 signaled the past start of each trial more accurately than mPFC. Postchoice CA1 activity modulated subsequent PFC activity, so rewarded actions were more likely to occur. Together, the results show that in changing circumstances, HPC retrospective codes modulate subsequent PFC coding, which in turn modulates HPC prospective codes that predict choices.

How Weighting by Past Vote Can Improve Estimates of Voting Intentions

Polling error for the 2020 US election was the highest in 40 years and no mode of surveying was unambiguously more accurate. This occurred amid several recent polling failures in other countries. Online panels, as the dominant method now used by pollsters to survey voters, are well-positioned to help reduce the level of bias in pre-election polls. Here, we present a case for those pollsters using online panels for pre-election polling to (re)consider using past vote choice (i.e., whom respondents voted for in the previous election) as a weighting variable capable of reducing bias in their election forecasts under the right circumstances. Our data are from an Australian pre-election poll, conducted on a probability-based online panel one month prior to the 2019 Australian federal election. Three different measures of recalled vote choice for the 2016 election were used in weighting the forecast of the 2019 election outcome. These were (1) a short-term measure of recall for the 2016 vote choice obtained three months after the 2016 election, (2) a long-term measure obtained from the same panelists three years after the 2016 election and (3) a hybrid measure with a random half of panelists allocated their short-term past vote measure for 2016 and the remainder their long-term measure. We then examined the impacts on the bias and variance of the resulting estimates of the 2019 voting intentions. Using the short-term measure of the 2016 recalled vote choice in our weighting significantly reduced the bias of the resulting 2019 voting intentions forecast, with an acceptable impact on variance, and produced less biased estimates than when using either of the other two past vote measures. The short-term recall measure also generally resulted in better estimates than a weighting approach that did not include any past vote adjustment. Implications for panel providers are discussed.

Using Cognitive Models to Understand and Counteract the Effect of Self-Induced Bias on Recommendation Algorithms

Abstract Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.

Examining municipalities’ choices of service delivery modes through the lens of historical institutionalism

ABSTRACT While the motives behind municipalities’ choices of service delivery modes may lie in the past, current research still pays little attention to history. Applied to the service delivery of two waste services in nine municipalities over the period 1962–2021, this study aims to overcome this limitation by bringing in historical institutionalism. Based on the analysis of a database covering 4,599 government documents and 30 semi-structured interviews, we show that past choices structure future choices by creating juridical and economic lock-ins. Path-dependent processes overrule other potential sources of change in choices, resulting in a high degree of stability of choices.

Exploring the role of conscientiousness on visualization-supported decision-making

Understanding how visualizations can support decision-making continues to be one of the most relevant challenges in current research. However, prior work provides limited knowledge regarding how the decision-maker profile and, in particular, psychological constructs affect decision-making. Weighing how conscientiousness affects one’s tendency to follow the rules and prioritize tasks, this work explores if this personality trait plays a role in visualization-supported decision-making. We asked participants to perform a series of multi-attribute choices using visualizations of high-dimensional data. Further, we study if the quality feedback of the past choice affects the decision-making process. Our results suggest that the feedback quality of past decisions affects how much individuals invest in data analysis and decision-making. In addition, user confidence is affected by conscientiousness scores, resulting in conscientious individuals changing their choices more often. Our findings provide new insights into the research challenges of analyzing decision-making in a visualization setting.

Habits, Triggers and Moral Formation

This article examines moral change, primarily through the lens of Summa Theologiae I-II 49–50. I argue that the specific difference Aquinas asserts between habits and dispositions allows for the possibility that virtuous habits can sometimes exist alongside problematic bodily dispositions. While in the typical case the actions that bring about a habit also bring about appropriate bodily dispositions, it is my contention that the cultivation of a habit need not eliminate all contrary bodily dispositions. This implies that one's past, whether it be one's pre-rational conditioning or past choices, can create bodily obstacles to the cultivation of virtue that later moral reformation may never entirely eliminate.

Motivation and investment

Abstract The purpose of the present study is to offer insights into how Spanish undergraduates, who were mid-way through an English-medium programme at a university in Catalonia, articulate their past decision to study an academic subject in English. Economics students (34 in total) completed an oral elicitation task and the monologues yielded were analysed using thematic analysis. Three dominant themes emerged: (1) The right fit for me; (2) To practise my English; and (3) English comes with benefits. Each of these themes is presented as a composite description of the students’ ideas about their past choice from the temporal position of the here-and-now, and explored through distinct but complementary social psychological and sociological lenses. The present study finds threads running through the students’ narratives of a near effortlessness to choose to learn in this way, as well as an understanding of the capital value of English. For some, their sole stated motive was to develop and preserve this linguistic asset.

Relations in Aesthetic Space: How Color Enables Market Positioning

Color is omnipresent, but organizational research features no systematic theory or established method for analyzing it. We develop a relational approach to color, conceptualizing it as a means of positioning relative to a reference group or style and validating it through a computational method for processing digital images. The research context is Norwegian black metal—a genre of extreme metal music that achieved notoriety in the early 1990s through band members’ criminal activity. Our analysis of 5,125 album covers between 1989 and 2019 confirms the alignment of aesthetic and music features and articulates the role of color in the construction of a relational identity based on forces of association and disassociation. Black metal bands associated with past color choices of non-black metal bands up to a point, after which they started to disassociate from them. The positioning is dynamic, pursuing adaptation to external events. Black metal bands reacted to their stigmatization in Norwegian society by increasing colorfulness and later returning to a darker aesthetic in defiance of the genre’s commercialization. Our analysis attests to color’s ability to organize producers’ exchange of information and attention, illustrating the interweaving of aesthetic features and relational processes in markets.

Transits in Oncology: A Protocol Study for a Therapy-Educational Training Built-In Intervention

The study “Transits in oncology” has been perfected with the collaboration of the UOC of Oncological Mammary Surgery of the Azienda Ospedaliero Universitaria Senese Siena, specifically by Prof. Donato Casella. The study means to analyze the impact of art-therapy interventions aimed at minimizing psychological distress in women with a diagnosis of breast cancer/mammary carcinoma (anxiety/depression), hence improving their psychophysical wellbeing. To this end, the study employs the evaluation of specific psychological parameters with the purpose of monitoring anxiety and depression levels, while investigating a potential correlation between the anxiety and depression levels and other psychological variables, such as alexithymia. The mammary carcinoma diagnosis, to all effects, constitutes an actual “disorienting dilemma” for the woman: it leads to questioning one’s way of life, and their past and future choices; the upheaval is conducive to a reflective phase that upsets one’s “expectations of meaningfulness”. The art-therapy intervention has been elaborated in a protocol that underscores its transformative methodology qualities: it aims to act on the regenerative potential of the turmoil, for an elaboration of trauma that does not negate it or further it (the feeling that nothing will change and everything will go back to the way it was before), but rather disrupts it. The final goal is to promote new existential practices, generating positive change towards self-awareness, stimulating the activation of one’s latent resources by accessing one’s symbolic world and one’s imagination.

Health-guided recipe recommendation over knowledge graphs

While the availability of large-scale online recipe collections presents opportunities for health consumers to access a wide variety of recipes, it can be challenging for them to discover relevant recipes. Whereas most recommender systems are designed to offer selections consistent with users’ past behavior, it remains an open problem to offer selections that can help users’ transition from one type of behavior to another, intentionally. In this paper, we introduce health-guided recipe recommendation as a way to incrementally shift users towards healthier recipe options while respecting the preferences reflected in their past choices. Introducing a knowledge graph (KG) into recommender systems as side information has attracted great interest, but its use in recipe recommendation has not been studied. To fill this gap, we consider the task of recipe recommendation over knowledge graphs. In particular, we jointly learn recipe representations via graph neural networks over two graphs extracted from a large-scale Food KG, which capture different semantic relationships, namely, user preferences and recipe healthiness, respectively. To integrate the nutritional aspects into recipe representations and the recommendation task, instead of simple fusion, we utilize a knowledge transfer scheme to enable the transfer of useful semantic information across the preferences and healthiness aspects. Experimental results on two large real-world recipe datasets showcase our model’s ability to recommend tasty as well as healthy recipes to users.

Surveying the surveyors to address risk perception and adaptive-behaviour cross-study comparability

Abstract. One of the key challenges for risk, vulnerability and resilience research is how to address the role of risk perceptions and how perceptions influence behaviour. It remains unclear why people fail to act adaptively to reduce future losses, even when there is ever-richer information available on natural and human-made hazards (flood, drought, etc.). The current fragmentation of the field makes it an uphill battle to cross-validate the results of existing independent case studies. This, in turn, hinders comparability and transferability across scales and contexts and hampers recommendations for policy and risk management. To improve the ability of researchers in the field to work together and build cumulative knowledge, we question whether we could agree on (1) a common list of minimal requirements to compare studies, (2) shared criteria to address context-specific aspects of countries and regions, and (3) a selection of questions allowing for comparability and long-term monitoring. To map current research practices and move in this direction, we conducted an international survey – the Risk Perception and Behaviour Survey of Surveyors (Risk-SoS). We find that most studies are exploratory in nature and often overlook theoretical efforts that would enable the comparison of results and an accumulation of evidence. While the diversity of approaches is an asset, the robustness of methods is an investment to be made. Surveyors report a tendency to reproduce past research design choices but express frustration with this trend, hinting at a turning point. To bridge the persistent gaps, we offer several recommendations for future studies, particularly grounding research design in theory; improving the formalisation of methods; and formally comparing theories and constructs, methods, and explanations while collecting the themes and variables most in use.

Management of common pool resources in a nation-wide experiment

Dilemmas related to the use of environmental resources concern diverse populations at local or global scales. Frequently, individuals are unable to visualize the consequences of their actions, where they belong in the decision-making line, and have no information about past choices or the time horizon. We design a new one-shot extraction game to capture these dynamic decisions. We present results from a nationwide common pool resource experiment, conducted simultaneously in eleven French cities, involving a total of 2813 participants. We examine, for the first time, the simultaneous impact of several variables on the amount of resource extracted: the local vs. the national scale of the resource, the size of the group (small vs. big), the low vs. high recovery rate of the resource, and the available information. We show that individuals significantly reduce extraction levels in local as compared to national level dilemmas and that providing recommendations on sustainable extraction amounts significantly improves the sustainability of the resource. Overall, women extract significantly less, but care more about preserving the local resource; older participants extract significantly more resources but extract less from the national resource. Our experiment also fulfills a science popularization pedagogical aim, which we discuss.