Trivial Decisions Research Articles

An approach for analysing the impact of data integration on complex network diffusion models

Abstract Complex networks are a powerful way to reason about systems with non-trivial patterns of interaction. The increased attention in this research area is accelerated by the increasing availability of complex network data sets, with data often being reused as secondary data sources. Typically, multiple data sources are combined to create a larger, fuller picture of these complex networks and in doing so scientists have to make sometimes subjective decisions about how these sources should be integrated. These seemingly trivial decisions can sometimes have significant impact on both the resultant integrated networks and any downstream network models executed on them. We highlight the importance of this impact in online social networks and dark networks, two use-cases where data are regularly combined from multiple sources due to challenges in measurement or overlap of networks. We present a method for systematically testing how different, realistic data integration approaches can alter both the networks themselves and network models run on them, as well as an associated Python package (NIDMod) that implements this method. A number of experiments show the effectiveness of our method in identifying the impact of different data integration setups on network diffusion models.

Taking Decisions Too Seriously: Why Maximizers Often Get Mired in Choices.

Maximizing is a topic that has received significant attention from researchers and corporate organizations alike. Although extensive previous research has explored how maximizers behave in a decision scenario, a fundamental question remains about why they prefer a larger assortment regardless of whether the decisions are important or not. This study attempts to explore the underlying mechanism of this phenomenon. Four surveys were conducted, and participants from Mturk or Credamo online platforms were recruited (N = 922). The maximizing tendency was measured by either maximization scale or maximizing tendency scale, and perceived importance and preference for a large assortment were measured in different decision scenarios. Across four studies, we find that maximizers perceive the same decision as more important than satisficers (Study 1), and perceived importance serves as the mechanism underlying the maximizers’ preference for a large assortment (Study 2). In other words, in maximizers’ perceptions and interpretations, even seemingly trivial decisions are important enough to spend great effort on a large assortment. We additionally identified a boundary condition for the effect – cost salience (Studies 3a and 3b). These findings illustrate a pioneering empirical exploration of the difference in the way maximizers and satisficers perceive their decision importance and the reason for maximizers’ preference for a large assortment.

Wild Dreams: Cultivating Change in and with Community

Wild Dreams:Cultivating Change in and with Community Leah Kalmanson (bio) I am truly humbled and astounded to find myself the grateful recipient of the wise insight, critical engagement, and creative elaboration provided by the three readers of my book, Boram Jeong, Martina Ferrari, and Eric S. Nelson. I begin with Boram Jeong's attention to the decolonizing trajectory of the book. Throughout my writing process, I sought to enact the provincialization of Europe1 by decentering Eurocentric discourse and, at times, actively ignoring it. The result, I suspected, would be that my very theories and methods would become unintelligible to the academic philosophical mainstream. At times, I worried to myself: does this book contribute to a "cross-cultural" philosophical project if other philosophers do not recognize it as philosophy? But, as Jeong points out, keeping philosophy recognizable is part of the problem. As she says, attempts to diversify philosophical practice may serve only to reinforce Eurocentrism by translating Asian, African, Indigenous, or Latin American traditions into Euro-American philosophical language. In contrast, my project, at the risk of unintelligibility, attempts the reverse: re-inscribing existential discourse to conform to East Asian models. I am especially gratified and validated by Jeong's appraisal that this "counter-mapping," as she says, serves the decolonizing purpose toward which I intended it. Jeong goes on to raise questions that remain, for me, unanswered and still urgent at the end of the book. As she asks, how are we to understand sociopolitical change under the speculative philosophy proposed in my project? Or, how does radical interconnectedness affect our understanding of our responsibilities under conditions of systemic injustice? Undoubtedly, many of the authors I engage—from the twentieth-century Buddhist nun Kim Iryŏp to the Song dynasty Ruist Zhu Xi—are confident that mental cultivation does exert an impact on the world. But, as Jeong reminds us, this impact is not necessarily liberatory. Presumably, the current conditions of inequity under which we live are, by my own analysis of "petty-mindedness," co-constituted by our mutually realized social orders. To the extent that liberatory change can be enacted, or new social conditions can be realized, the book's claim about pairing practice with theory cannot itself remain theoretical. Jeong concludes that educational and institutional reforms must be part of the conversation on self-cultivation practices as methods for existential transformation. Her focus on the confluence of the personal and the institutional enriches the political dimension of my overall project. [End Page 290] Following Jeong's attention to decolonizing trajectories, I am deeply inspired by Martina Ferrari's engagement with Mariana Ortega's notion of "hometactics" or "microtechniques" related to everyday activities: how we arrange our living spaces, how we prepare our food, how we speak (especially via multilingualism and code switching). As Ferrari points out, these hometactics are precisely such embodied practices of meaning-making that I seek in the book. Ortega's attention to specific contexts, to everyday choices in home, food, and speech, reminds me of Ruist attention to ritual practice, which is sometimes stereotyped as fastidious or overly rigid. But, as Ferrari reminds us, everyday choices matter—seemingly trivial decisions regarding what to wear, what to say, or how to eat present us moment to moment with options for world-making that have a direct impact on self and other. Ruist attention to detail is not trivial but asks us to take such live options seriously. Acknowledging and developing the ritualized aspects of these everyday microtechniques can help concretize one of the prevailing currents of the book, which is its tendency to blur Eurocentric divisions between the philosophical, the religious, and the spiritual. Along this line, Ferrari's turn to the work of Gloria Anzaldúa at the end is equal parts brilliant and arresting. As Ferrari says, even those theorists who claim to resist mind/body dualism rarely broach the most transgressive aspects of Anzaldúa's "spiritual activism" via her engagement with shamanistic practice. This topic demands attention. As someone who frequently is called upon to teach introductory religion courses, I am routinely disappointed by the quasi-Hegelian ordering of textbooks according to so-called "world religions." As Tomoko...

Approaching Scheduling Problems via a Deep Hybrid Greedy Model and Supervised Learning

Abstract Scheduling still constitutes a challenging problem, especially for complex problem settings involving due dates and sequence-dependent setups. The majority of existing approaches use heuristics or meta-heuristics, like Genetic Algorithms or Reinforcement Learning. We show that a supervised learning framework can learn and generalize from generated optimal target schedules, which amplifies convergence compared to unsupervised methods. We present a deep hybrid greedy framework, which can predict near-optimal schedules by utilizing the following key mechanisms: (i) Through the interplay between heuristics and a deep neural network our hybrid model can combine the benefits. Specifically, complex patterns from optimal schedules can be learned by a neural network. We reduce the computational costs by outsourcing trivial decisions to heuristics and therefore, allowing consistent decisions during training. (ii) The problem complexity can be reduced, by employing a greedy prediction scheme, where one job at a time is predicted. (iii) We propose a re-scheduling mechanism for idle jobs, which enables long-term cost reduction and renders the framework reactive and dynamic. Through the heuristics and the neural network, our model is real-time capable during inference. We compare our model against prevailing scheduling heuristics and our model outperformed one of them in terms of makespan and lateness minimization. The key purpose of this work is to give a proof of concept, that supervised learning is applicable for complex scheduling problems.

Event-Related Changes In EEG Spectral Power Corresponding to Creative and Trivial Decisions

The study aimed to explore EEG correlates of a person’s creative (non-trivial) and trivial decisions finding in conditions of associative search by means of event-related synchronization/event-related desynchronization (ERS/ERD) spectral power changes analysis. Sixty subjects had to fulfill RAT (Remote associative test) alike tasks while EEG registering. There were no differences in time responses while remote association and trivial decisions search (around 3 seconds in both case). An increase in ERS was observed for the central frequencies 4 Hz at F7 site (1500-1000 ms before note of decision), 3 Hz at the Fz site (500-1000 ms before note of decision), and 3.5 Hz at T3 site (in the last 500 ms before decision). Obtained findings stressing the role of low frequency bands (theta and delta) in wide associative field search leading for creative and original decisions.

The (Internet) Information Inequality Machine?

Advances in information technology have fundamentally altered how we make decisions, as well as the range of alternative options we might consider when deciding. From fairly trivial decisions to important ones, our decisions are increasingly informed by online expert or “crowd-sourced” ratings. The use of this ratings information is no surprise as it helps reduce uncertainty. As the prevalence and usage of ratings information increase, so too does the concentration of income and wealth as “superstars” garner an increasing share of returns. There is reason to believe these trends are entwined as predicted by seminal theories in both sociology (Merton’s “Matthew Effect”) and economics (Rosen’s star markets). Both Merton’s and Rosen’s theories imply that increasing availability of ratings information should exacerbate inequality. Viewed from a different perspective, however, increasing availability of ratings information may help facilitate comparison of entities on some dimensions, thereby allowing for differentiation and consumer sorting on other dimensions. This, in turn, should reduce inequality. I test these propositions in the context of the NYC restaurant industry from 1994 – 2013 using restricted-access administrative data and matched time-varying restaurant ratings. This period purposefully envelopes Yelp’s market entry and expansion, and the greater accessibility of ratings information it afforded. Results are statistically large and economically meaningful, and show heterogeneous information effects across different markets. For those market segments with less local information (e.g., tourists) making larger cultural purchases, ratings exacerbate sales disparities between competitors. Theoretical implications for both the Matthew Effect and Star Markets theories are discussed throughout.

An experimental examination of measurement disparities in public climate change beliefs

The extent to which Americans—especially Republicans—believe in anthropogenic climate change (ACC) has recently been the subject of high profile academic and popular disagreement. We offer a novel framework, and experimental data, for making sense of this debate. Using a large (N = 7,019) and demographically diverse sample of US adults, we compare several widely used methods for measuring belief in ACC. We find that seemingly trivial decisions made when constructing questions can, in some cases, significantly alter the proportion of the American public who appear to believe in human-caused climate change. Critically, we find that some common measurement practices may nearly double estimates of Republicans’ acceptance of human-caused climate change. We conclude by discussing how this work can help improve the consumption of research on climate opinion.

The (Internet) Information Inequality Machine?

Advances in information technology have fundamentally altered how we make decisions, as well as the range of alternative options we might consider when deciding. From fairly trivial decisions to important ones, our decisions are increasingly informed by online expert or “crowd-sourced” ratings. The use of this ratings information is no surprise as it helps reduce uncertainty. As the prevalence and usage of ratings information increase, so too does the concentration of income and wealth as “superstars” garner an increasing share of returns. There is reason to believe these trends are entwined as predicted by seminal theories in both sociology (Merton’s “Matthew Effect”) and economics (Rosen’s star markets). Both Merton’s and Rosen’s theories imply that increasing availability of ratings information should exacerbate inequality. Viewed from a different perspective, however, increasing availability of ratings information may help facilitate comparison of entities on some dimensions, thereby allowing for differentiation and consumer sorting on other dimensions. This, in turn, should reduce inequality. I test these propositions in the context of the NYC restaurant industry from 1994 – 2013 using restricted-access administrative data and matched time-varying restaurant ratings. This period purposefully envelopes Yelp’s market entry and expansion, and the greater accessibility of ratings information it afforded. Results are economically meaningful, and show heterogeneous information effects across different markets. For those market segments with less local information (e.g., tourists) making larger cultural purchases, ratings exacerbate sales disparities between competitors. Theoretical implications for both the Matthew Effect and Star Markets theories are discussed throughout.

Numerate decision makers don’t use more effortful strategies unless it pays: A process tracing investigation of skilled and adaptive strategy selection in risky decision making

AbstractThe present study investigated skilled and adaptive strategy selection in risky decision making. We proposed that people with high objective numeracy, a strong predictor of general decision making skill, would have a broad repertoire of choice strategies and adaptively select these strategies depending on the importance of the decision. Thus more objectively numerate people would maximize their effort (e.g., invest more time) in important, high-payoff decisions and switch to a simple, fast heuristic strategy in trivial decisions. Subjective numeracy would, by contrast, be more closely related to interest in problem solving for its own sake and would not yield such an effect of importance. Participants made twelve high-payoff choices and twelve low-payoff choices in binary two-outcome gambles framed as gains. We measured objective and subjective numeracy using standard measures. Results showed that people with high subjective numeracy generally maximized the expected value (EV) in all decisions. In contrast, participants with high objective numeracy maximized EV only when choice problems were meaningful (i.e., they could result in high payoffs). When choice problems were trivial (i.e., choosing the normatively better option would not result in a large payoff), more objectively numerate participants made choices consistent with faster, more frugal heuristic strategies.

About a Hundred Years of Creativity in Decision Making

Can all the methods people invented to do decision making be right all the time? Maybe for making trivial decisions, but not in the case of those that involve dependence and feedback and need complete analysis of benefits, opportunities, costs and risks to get an overall justifiable outcome.

Positioning patient status monitors in a family waiting room.

To determine where to place patient status displays for family members in the operating room family waiting room at The Children's Hospital of Philadelphia. We calculated the percentage of seats from which wall monitors placed in hypothetical positions would be usable. We validated the usability of the new monitors by observing nonemployees' use of monitors in the waiting room 1 week before and 1 week after implementation. Compared to the legacy monitor, the new monitors were observed to be used from more locations within the waiting room and more people were observed to use the new monitors soon after entering the waiting room. Seemingly trivial decisions like where in a waiting room to place monitors can be informed by careful data collection and the consequences can observably impact communication between hospital staff and family members waiting for loved ones in surgery.

Pride and Prejudice in Contemporary Marriages: On the Hidden Constraints to Individualisation at the Crossroad of Tradition and Modernity

Pride and Prejudice in Contemporary Marriages: On the Hidden Constraints to Individualisation at the Crossroad of Tradition and Modernity

Decision Quicksand: How Trivial Choices Suck Us In

People often get unnecessarily mired in trivial decisions. Four studies support a metacognitive account for this painful phenomenon. Our central premise is that people use subjective experiences of difficulty while making a decision as a cue to how much further time and effort to spend. People generally associate important decisions with difficulty. Consequently, if a decision feels unexpectedly difficult, due to even incidental reasons, people may draw the reverse inference that it is also important and consequently increase the amount of time and effort they expend. Ironically, this process is particularly likely for decisions that initially seemed unimportant because people expect them to be easier (whereas important decisions are expected to be difficult to begin with). Our studies demonstrate that unexpected difficulty not only causes people to get caught up in unimportant decisions but also to voluntarily seek more options, which can increase decision difficulty even further.

Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data

Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters—and it is generally used only for count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.

Are Individual Rights Possible?

What are your rights? Should you or should society determine whether you can wear a brown or blue shirt to class? Let's be more provocative: should you or should society decide whether you can read that raunchy article? The answers seem obvious-at least they did until a quarter of a century ago when A. Sen ([18], [19]) analyzed this question with an axiomatic formulation. His highly disturbing mathematical conclusion casts doubt on the rights of individuals to make even seemingly trivial decisions of this type. How can Sen's conclusion, which directly confronts our daily actions, be correct? As his proof is accurate, it is understandable that his disquieting assertion continues to concern experts from mathematics, economics, philosophy, and political science, among other areas. While the response has created a sizable literature, none of the papers provides a way out. Instead, the problem has become similar to a doll made of fresh tar; the more it is embraced, the greater the mess that is discovered. In this essay, a surprisingly elementary explanation of Sen's problem is offered. (See [16] for a complete description.) It only uses only the kind of introductoiy mathematics too many students try to skip due to persistent rumors that this busy work might convert brain matter into mushy oatmeal. Let me explain.

A New Threat to Pregnant Women's Autonomy

Courts and legislatures are increasingly being called upon to restrict the autonomy of pregnant women by requiring them to behave in ways that others determine are best for the fetuses they carry. The state should not attempt to transform pregnant women into ideal baby-making machines. Pregnant women make decisions about their behavior in the context of the rest of their lives, with all the attendant complexities and pressures. Our interest in helping future children by improving prenatal care would best be furthered by helping pregnant women to make informed, less constrained choices, not by punishing women or depriving them of choices altogether.

Is There a ‘Road to Serfdom’?

A PREDOMINANT THEME OF TWENTIETH-CENTURY CLASSICAL liberal thought is the claim that piecemeal acts of intervention by government in a free economy and society will, if continued over an unspecified period of time, bring about the transformation of that society into a totalitarian regime in which all but the most trivial decisions affecting an individual are taken by the state. The crucial feature of the liberal's argument is that this process generates an outcome which was not intended by the originators of the acts of intervention: it is the method or mechanism of intervention itself which produces a state of affairs undesired by both non-statists and (at least moderate) collectivists alike. Thus, in addition to general economic and moral arguments that a classical liberal might raise against, say, a nationalized health service, he also maintains that an abridgement of the right to provide health care privately, which the collectivist does not value, must set in motion a process which culminates in the direction of medical personnel, and the prevention of the emigration of doctors by the state, to which, presumably, the moderate collectivist would himself object.

System for computer aided processing of phonetic measurements

A set of computer programs is described which will sample and average e.g. intonation, air pressure, or EMG activity. Data processing is essentially user controlled, but trivial decisions may be left to the computer if this is desired. The programs are accessible to users with little experience with computers.

Substantive conclusions are dependent upon techniques of measurement

Two recent reviews of mathematical modeling in social psychology (Abelson, 1967; Rosenberg, 1968) indicate that efforts to develop formal analyses of sequential social behavior will most likely accelerate in coming years, as will research devoted to determining which of the several competing models provides the best representation of the way social interaction is sequentially organized. The purpose of our paper is to highlight two methodological difficulties which may beset the investigators in such research. We have evidence that apparently trivial decisions—such as how to operationally define one's unit of observation or how often to observe the units so defined—may have profound effects on the conclusions drawn from the data. The formal model chosen as best fitting the data may be rejected or found adequate depending upon the measurement decisions adopted. Two empirical analyses are cited below to support this contention: The first shows how closely predictions of behavior based on past performance are dependent upon the level of complexity of the units utilized in the analyses. The second shows that a Markov model of sequential dependencies in conversational speech was supported using one set of observational sampling rates but rejected when different rates were used.