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Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers

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Abstract

We introduce a general framework for modeling functionally diverse problem-solving agents. In this framework, problem-solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem-solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best-performing agents. This result relies on the intuition that, as the initial pool of problem solvers becomes large, the best-performing agents necessarily become similar in the space of problem solvers. Their relatively greater ability is more than offset by their lack of problem-solving diversity.

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... The following summarizes the highlights of mainstream science of CI research in order to establish a foundation to expand the applicability and capability of CI. Readers will find more resources in other articles within this special issue on CI and in the following references: a review of forty years of research on collective processes in organizations (Williams and O'Rielly, 1996) that capture a traditional view of diversity, particularly the challenges; the extensive and self-consistent analysis by Scott Page and his collaborators (Hong and Page, 2001, Page, 20051 , Page, 2007, Hong and Page, 2011, Shalizi, 2005, a review of modern web-based collective decision methods (Watkins and Rodriguez, 2008); and how the Internet may finally realize the full potential of the collective ideals of the Age of Enlightenment (Rodriguez and Watkins, 2009). When individuals or groups solve problems, they use different preferences, biases, experiences, or heuristics in their solution to the problem, thereby, introducing a collective diversity of solution approaches and contributions. ...
... The following summarizes the highlights of mainstream science of CI research in order to establish a foundation to expand the applicability and capability of CI. Readers will find more resources in other articles within this special issue on CI and in the following references: a review of forty years of research on collective processes in organizations (Williams and O'Rielly, 1996) that capture a traditional view of diversity, particularly the challenges; the extensive and self-consistent analysis by Scott Page and his collaborators (Hong and Page, 2001, Page, 20051 , Page, 2007, Hong and Page, 2011, Shalizi, 2005, a review of modern web-based collective decision methods (Watkins and Rodriguez, 2008); and how the Internet may finally realize the full potential of the collective ideals of the Age of Enlightenment (Rodriguez and Watkins, 2009). When individuals or groups solve problems, they use different preferences, biases, experiences, or heuristics in their solution to the problem, thereby, introducing a collective diversity of solution approaches and contributions. ...
... The individual ability factor: Individuals in the collective must have a minimum degree of ability in solving the problem in order for the collective solution to be accurate. A major contribution of Scott Page to the science of CI is the proof that these two factors are quantitatively coupled for certain types of problems (Page 2007, Hong andPage 2011), called the Diversity Prediction theorem: Collective error = Average individual error -Collective diversity. ...
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Almost every generation imagines itself on a precipice, where problems seem too complex to solve and the future is bleak. Yet, society survives each time, often reinventing itself. With global climate change, dying oceans, democide, killer epidemics, and other modern crises, humanity may truly be at the precipice where our actions in the next decade will determine the future of humankind. In this article, we discuss how proactive collective intelligence is the game-changing resource that offers hope, extending its role beyond the "wizard behind the curtain" in the past. Based on research over the last two decades, the advantages and limitations of collective intelligence can be now understood. When added to the traditional spectrum of problem-solving methodologies and leadership , collective intelligence using diverse groups can extend the complexity of problems that can be solved-defining when and how diverse collectives can outperform experts, while being more robust. Because expression and compatibility of diversity are required for collective intelligence, we show how managing social identity (us versus other) is the key to enabling diversity, particularly when diverse views are in conflict or contain biases. We conclude that future methodologies may need to embrace biases that have embedded truths captured within situated understandings of the complex problem domain. Finally, a practical example of a grand challenge project illustrates the implementation of above concepts to solve a problem of international importance. This project used advanced risk assessment methods, similar to Open Spaces and World Cafe, that efficiently captured diverse knowledge, even when participants were biased and in conflict.
... Another argument for methodological plurality comes from research on cognitive diversity [48]. This line of research argues that a group of diverse problem solvers are (under a particular set of assumptions) more effective than a group of individually effective problem solvers. ...
... These arguments can be extended to epistemic communities [116], although some caution is required. In particular, Grim et al. note that the results proved in [48] show that under certain circumstances, a group of diverse problem solvers (models) can outperform a homogeneous group of individually effective problem solvers [43]. They also demonstrate that true "experts" (as opposed to just more-effective problem solvers) can be substantially better than a diverse group of non-experts. ...
... The diversity results in [48] apply equally to ML methods: a diverse ensemble of models can more effectively solve problems than an ensemble of individually highly effective models. This fact has also been noted independently within ML where diversity of base classifiers empirically improves boosting [70] and diverse deep ensembles empirically improve performance and uncertainty quantification [59]. ...
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"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many researchers are excited that similar model architectures can be successfully deployed across diverse applications in vision, language and beyond. We consider the benefits and risks of these waves of unification on both epistemic and ethical fronts. On the epistemic side, we argue that many of the arguments in favor of unification in the natural sciences fail to transfer over to the machine learning case, or transfer over only under assumptions that might not hold. Unification also introduces epistemic risks related to portability, path dependency, methodological diversity, and increased black-boxing. On the ethical side, we discuss risks emerging from epistemic concerns, further marginalizing underrepresented perspectives, the centralization of power, and having fewer models across more domains of application
... Some existing approaches are proposed to automatically select algorithms and construct suitable portfolios to solve black-box optimization problem sets. The conclusions in [27,28] state some basic rules of algorithm portfolio composition. The work of [27] finds that the essence of constructing an algorithm portfolio is to choose algorithms with diversity. ...
... The conclusions in [27,28] state some basic rules of algorithm portfolio composition. The work of [27] finds that the essence of constructing an algorithm portfolio is to choose algorithms with diversity. The results from [28] show that complementary algorithm combinations are more likely to improve overall performance. ...
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As an individual algorithm rarely outperforms all kinds of optimization problems, algorithm portfolios are proposed to combine algorithms and take advantage of their strengths which fits well the prevalent theme of memetic computing. When there are many algorithms to choose from, the possibilities of algorithm combinations are numerous. Therefore, composing an algorithm portfolio which performs well for a given problem class is essential. In this paper, based on a problem set drawn from any unknown problem class according to an unknown probability distribution, we propose a general method to automatically accomplish portfolio construction. The problem set is used as training data for our method to learn an algorithm portfolio suitable for solving the underlying problem class. To construct the portfolio, algorithms are chosen and added one by one. We first find the best-performing algorithm based on its average rank of solving the training problem set. Its most complementary algorithm is then selected by applying the Pearson correlation coefficient of fitness values at the first hitting time. The method iterates to compose the portfolio with more and more algorithms until there is no more improvement. The experimental results indicate the effectiveness of this approach to select well-cooperated algorithms, and the composed portfolio is shown to have the best rank compared to individual algorithms, elite portfolios and comparison algorithms.
... Using a Bayesian modeling framework, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of human and machine classifiers while taking into account the unique ways human and algorithmic confidence is expressed. (34)(35)(36)(37)(38). Similarly, work on ensemble methods in machine learning has shown that combining classifiers is particularly effective when they are less correlated in their predictions (39)(40)(41)(42). ...
... Previous work has shown the benefits of separately combining the predictions of diverse machine classifiers (39)(40)(41)(42) or groups of people (34)(35)(36)(37)(38). In this work, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of machine and human classifiers. ...
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Significance With the increase in artificial intelligence in real-world applications, there is interest in building hybrid systems that take both human and machine predictions into account. Previous work has shown the benefits of separately combining the predictions of diverse machine classifiers or groups of people. Using a Bayesian modeling framework, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of human and machine classifiers while taking into account the unique ways human and algorithmic confidence is expressed.
... We know that broader networks that go beyond self-identified student affinity groups are better for student retention and success [55]. Furthermore, the work of Hong and Page [23] tells us that diverse groups of problem solvers can outperform high-ability groups. Many other works also emphasize the importance of diversity in groups [54,61]. ...
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... Scientific Progress. By now, the research on the relationship between diversity and scientific progress is conclusive: Quality of science is greatly improved by a greater diversity of scientists (19,36,37). Racial, ethnic, gender, identity, ability, and other types of diversity lead to increased creativity and innovation. ...
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The history of the scientific enterprise demonstrates that it has supported gender, identity, and racial inequity. Further, its institutions have allowed discrimination, harassment, and personal harm of racialized persons and women. This has resulted in a suboptimal and demographically narrow research and innovation system, a concomitant limited lens on research agendas, and less effective knowledge translation between science and society. We argue that, to reverse this situation, the scientific community must reexamine its values and then collectively embark upon a moonshot-level new agenda for equity. This new agenda should be based upon the foundational value that scientific research and technological innovation should be prefaced upon progress toward a better world for all of society and that the process of how we conduct research is just as important as the results of research. Such an agenda will attract individuals who have been historically excluded from participation in science, but we will need to engage in substantial work to overcome the longstanding obstacles to their full participation. We highlight the need to implement this new agenda via a coordinated systems approach, recognizing the mutually reinforcing feedback dynamics among all science system components and aligning our equity efforts across them.
... For the purposes of this discussion, URMs include the following: biracial individuals and people of color (BIPOCs), women, members of the lesbian, gay, bisexual, transgender and queer (LGBTQ) community, and individuals with disabilities. A lack of representation in STEM fields presents significant ethical and societal challenges not only within these fields, but also for the larger economy and educational systems that support STEM professions (Hong and Page 2004;Grossman and Porche 2013). As broad consumer use of technology increases, and findings from scientific research and development become more publically available, the need for equitable dissemination of scientific information and transparent communication also increases (Hodson 2003). ...
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The last several years has seen numerous initiatives rise to increase representation of under-represented minority groups (URMs) in Science Technology Engineering and Mathematics (STEM) professions. Yet despite these efforts, disparities between students educated in the sciences and professionals in these fields persist. One significant contributor to these disparities is a lack of funding and support for science educational resources, particularly in low-income communities and school districts. The following paper evaluates the efficacy of an elementary school STEM educational program, known as the Young Scientists Program (YSP), which serves over 1400 elementary school students in seven Title I schools within the Los Angeles Unified School District (LAUSD). The specific aim of this project was to determine whether targeting a younger student population is more effective at promoting a greater sense of self-efficacy within science, ultimately encouraging students to see themselves as potential future scientists regardless of their socioeconomic status and cultural background. Students who participated in the program completed a pre- and post-program “Draw A Scientist Test” (DAST) and general Science Interest Survey. Statistical analysis of the quantitative data from these instruments showed a significant increase in the number of students who drew scientists that represented themselves and/or members of their community, and more positive attitudes toward STEM after participating in the YSP. These findings provide a model for continued program evaluation and comparison of previous years’ data for the YSP, as well as for evaluation of similar elementary school science programs.
... In particular, the diversity of the local communities needs to be represented in positions of responsibility in local and regional ecosystem management, monitoring and research to ensure wholeof-community support for the conservation goals and processes. If well supported, diverse decision-making teams have greater capacity to generate and explore innovative approaches to challenges and show greater thoroughness of decision-making processes and accuracy of assessments (Cheruvelil et al. 2014;Hong and Page 2004;Phillips et al. 2014), which are fundamental for improving marine ecosystem management. ...
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Marine ecosystems and their associated biodiversity sustain life on Earth and hold intrinsic value. Critical marine ecosystem services include maintenance of global oxygen and carbon cycles, production of food and energy, and sustenance of human wellbeing. However marine ecosystems are swiftly being degraded due to the unsustainable use of marine environments and a rapidly changing climate. The fundamental challenge for the future is therefore to safeguard marine ecosystem biodiversity, function, and adaptive capacity whilst continuing to provide vital resources for the global population. Here, we use foresighting/hindcasting to consider two plausible futures towards 2030: a business-as-usual trajectory (i.e. continuation of current trends), and a more sustainable but technically achievable future in line with the UN Sustainable Development Goals. We identify key drivers that differentiate these alternative futures and use these to develop an action pathway towards the desirable, more sustainable future. Key to achieving the more sustainable future will be establishing integrative (i.e. across jurisdictions and sectors), adaptive management that supports equitable and sustainable stewardship of marine environments. Conserving marine ecosystems will require recalibrating our social, financial, and industrial relationships with the marine environment. While a sustainable future requires long-term planning and commitment beyond 2030, immediate action is needed to avoid tipping points and avert trajectories of ecosystem decline. By acting now to optimise management and protection of marine ecosystems, building upon existing technologies, and conserving the remaining biodiversity, we can create the best opportunity for a sustainable future in 2030 and beyond.
... Thus, it is preferable to explore the entire design space. Given this context, the tendency of the expert designers to gravitate towards particular solutions could be balanced with an option to work with a 'crowd', to obtain a diverse set of independent attempts (Hong & Page 2004;Fu et al. 2013). This is reflected in practice, where grippers are often sold separately from manipulators because there are both values to application customization and there are many more unconventional approaches being explored (e.g., gecko grippers and electromagnets). ...
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This article proposes the solver-aware system architecting framework for leveraging the combined strengths of experts, crowds and specialists to design innovative complex systems. Although system architecting theory has extensively explored the relationship between alternative architecture forms and performance under operational uncertainty, limited attention has been paid to differences due to who generates the solutions. The recent rise in alternative solving methods, from gig workers to crowdsourcing to novel contracting structures emphasises the need for deeper consideration of the link between architecting and solver-capability in the context of complex system innovation. We investigate these interactions through an abstract problem-solving simulation, representing alternative decompositions and solver archetypes of varying expertise, engaged through contractual structures that match their solving type. We find that the preferred architecture changes depending on which combinations of solvers are assigned. In addition, the best hybrid decomposition-solver combinations simultaneously improve performance and cost, while reducing expert reliance. To operationalise this new solver-aware framework, we induce two heuristics for decomposition-assignment pairs and demonstrate the scale of their value in the simulation. We also apply these two heuristics to reason about an example of a robotic manipulator design problem to demonstrate their relevance in realistic complex system settings.
... Considering that empiricism is the theory that all knowledge comes from the senses, white empiricism is therefore an "antiempirical disposal of data" (Prescod-Weinstein, 2020, p. 423). Arguments in the diversity, equity, and inclusion (DEI) space often ask the audience to consider how much the sciences are missing due to this disposal and dismissal of black women and groups who are othered (Gibbs Jr., 2014;Graves et al., 2022;Hong & Page, 2004;Jimenez et al., 2019;Ouimet, 2015). While it is true that the physics community as a whole loses when it routinely bars certain demographics from entering and contributing, what matters here is the black women deserve a seat at the table for no reason other than they have an interest they want to pursue. ...
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The purpose of this white paper is to lay out the impacts of policing and gatekeeping in STEM, illustrated with lived experiences of scientists of color who are achieving despite the daunting challenges they face.
... Many kinds of research showed that adding diversity to individual judgments in many cases leads to the cancellation of individual biases through the aggregation of opinions (Keuschnigg and Ganser 2017;Larrick and Soll 2006;Lorenz et al. 2011). Thus, even if error-prone and/or uncertain judges are included in the crowd, the wisdom of crowds exploits the law of large numbers and cancellation of contradictory errors, and can outperform homogeneous expert judgments (Grofman et al. 1983;Hong and Page 2004). In addition to that, many platforms for crowdsourcing data are available and crowd opinions may be collected at a low cost. ...
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Crowdsourcing and crowd voting systems are being increasingly used in societal, industry, and academic problems (labeling, recommendations, social choice, etc.) due to their possibility to exploit “wisdom of crowd” and obtain good quality solutions, and/or voter satisfaction, with high cost-efficiency. However, the decisions based on crowd vote aggregation do not guarantee high-quality results due to crowd voter data quality. Additionally, such decisions often do not satisfy the majority of voters due to data heterogeneity (multimodal or uniform vote distributions) and/or outliers, which cause traditional aggregation procedures (e.g., central tendency measures) to propose decisions with low voter satisfaction. In this research, we propose a system for the integration of crowd and expert knowledge in a crowdsourcing setting with limited resources. The system addresses the problem of sparse voting data by using machine learning models (matrix factorization and regression) for the estimation of crowd and expert votes/grades. The problem of vote aggregation under multimodal or uniform vote distributions is addressed by the inclusion of expert votes and aggregation of crowd and expert votes based on optimization and bargaining models (Kalai–Smorodinsky and Nash) usually used in game theory. Experimental evaluation on real world and artificial problems showed that the bargaining-based aggregation outperforms the traditional methods in terms of cumulative satisfaction of experts and crowd. Additionally, the machine learning models showed satisfactory predictive performance and enabled cost reduction in the process of vote collection.
... Creativity is regarded as a social construct in the literature (Amabile, 1983a;Amabile, Goldfarb, & Brackfleld, 1990), and the contemporary research community commonly refers to the traditional paradigm of the lone inventor as a "myth" (Lemley, 2012). Studies from psychology show that groups of people with diverse backgrounds provide high-quality ideas and can outperform skilled experts (Hong & Page, 2004). ...
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This article-based doctoral thesis explores the stakeholder perspectives and experiences of crowdsourced creative work on two of the leading crowdsourcing platforms. The thesis has two parts. In the first part, we explore creative work from the perspective of the crowd worker. In the second part, we explore and study the requester's perspective in different contexts and several case studies. The research is exploratory and we contribute empirical insights using survey-based and artefact-based approaches common in the field of Human-Computer Interaction (HCI). In the former approach, we explore the key issues that may limit creative work on paid crowdsourcing platforms. In the latter approach, we create computational artefacts to elicit authentic experiences from both crowd workers and requesters of crowdsourced creative work. The thesis contributes a classification of crowd workers into five archetypal profiles, based on the crowd workers' demographics, disposition, and preferences for creative work. We propose a three-part classification of creative work on crowdsourcing platforms: creative tasks, creativity tests, and creativity judgements (also referred to as creative feedback). The thesis further investigates the emerging research topic of how requesters can be supported in interpreting and evaluating complex creative work. Last, we discuss the design implications for research and practice and contribute a vision of creative work on future crowdsourcing platforms with the aim of empowering crowd workers and fostering an ecosystem around tailored platforms for creative microwork. Keywords: creative work, creativity, creativity support tools, crowdsourcing
... Still, this modification did not improve the performance of the centralized groups significantly, suggesting that at least in groups with 21 members, the superior abilities of a few well-connected nodes are not sufficient to compensate for the lack of communication. These findings further support Hong and Page's [26] thesis that group structures favouring diversity trump the abilities of the individual members. ...
Article
We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively inf luenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication among agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly inf luenced by the increase of her abilities.
... An important question is therefore how the composition of the crowd affects the performance of the work it does. Hong and Page (2004) demonstrate that, given a large pool of potential participants, diversity in "how people represent problems and how they go about solving them" (Hong and Page, 2004, 16385) is even more important than individual expertise or ability. Using the case of Wikipedia, Shi et al. (2019) show that ideologically diverse groups produce higher-quality content, not only on political topics but more generally. ...
Article
This element shows, based on a review of the literature, how digital technology has affected liberal democracies with a focus on three key aspects of democratic politics: political communication, political participation, and policy-making. The impact of digital technology permeates the entire political process, affecting the flow of information among citizen and political actors, the connection between the mass public and political elites, and the development of policy responses to societal problems. This element discusses how digital technology has shaped these different domains, identifies areas of research consensus as well as unresolved questions, and argues that a key perspective involves issue definition, that is, how the nature of the problems raised by digital technology is subject to political contestation.
... Abundant research has found in lab experiments, theoretical and computational models, and real-world problem-solving scenarios, the phenomenon of a "diversity bonus"-that a diverse group performs better than a homogenous group (Page, 2019;Aminpour et al., 2021). Some even find a diverse group of nonexperts can outperform a homogeneous group of experts (Hong & Page, 2004). For a group of diverse agents to work together, an important aspect is cognitive alignment, such as commitment to group goals and shared beliefs (Krafft, 2019). ...
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Academic and philanthropic communities have grown increasingly concerned with global catastrophic risks (GCRs), including artificial intelligence safety, pandemics, biosecurity, and nuclear war. Outcomes of many risk situations hinge on the performance of human groups, such as whether democratic governments and scientific communities can work effectively. We propose to think about these issues as Collective Intelligence (CI) problems -- of how to process distributed information effectively. CI is a transdisciplinary perspective, whose application involves humans and animal groups, markets, robotic swarms, collections of neurons, and other distributed systems. In this article, we argue that improving CI can improve general resilience against a wide variety of risks. Given the priority of GCR mitigation, CI research can benefit from developing concrete, practical applications to global risks. GCR researchers can benefit from engaging more with behavioral sciences. Behavioral researchers can benefit from recognizing an opportunity to impact critical social issues by engaging with these transdisciplinary efforts.
... By designing Sensible AI systems with dissensus-centric features, we can increase the likelihood that someone raises a red flag given early signals of a failure situation. Prior work has implemented adversarial design in the form of red teaming in technical and social ways (e.g., adversarial attacks for testing and promoting cybersecurity [2], and forming teams with collective diversity and supporting deliberation [33,43,44], respectively). Here, HRO principles of reluctance to simplify, commitment to resilience, and deference to expertise can be observed in practice. ...
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Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact -- an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick's sensemaking theory, which focuses on who the explanation is intended for. Recent work has advocated for the importance of understanding stakeholders' needs -- we build on this by providing concrete properties (e.g., identity, social context, environmental cues, etc.) that shape human understanding. We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.
... To ensure high-quality work in research and clinical contexts, our field will benefit from greater diversity among the professionals in the field. There is evidence of better decision-making and the production of higher quality science when research teams or problem-solving groups are diverse (Antonio et al., 2004;Campbell et al., 2013;Hong & Page, 2004). Trust and engagement in research is also facilitated by having culturally-congruent, diverse research teams (e.g., George et al., 2014;Sierra-Mercado & Lázaro-Muñoz, 2018). ...
... The widely used method to understand user perceptions for technology acceptance in literature is the use of questionnaires. However, this approach has several disadvantages of bias and lack of transparency which ultimately leads to wrong observations of user perceptions [22]. To overcome these shortcomings in analyzing user perceptions for technology acceptance, several studies proposed to analyze online social media content [19,33,51]. ...
Article
Smart Border Control (SBC) technologies became a hot topic in recent years when the European Union (EU) Commission announced the Smart Borders Package to improve the efficiency and security of the border crossing points (BCPs). Although, BCPs technologies have potential benefits in terms of enabling traveller' data processing, they still lead to acceptability and usability challenges when used by travelers. Success of technologies depends on user acceptance. Sentiment analysis is one of the primary techniques to measure user acceptance. Although, there exists variety of studies in literature where sentiment analysis has been used to understand user acceptance in different domains. To the best of our knowledge, there is no study where sentiment analysis has been used for measuring the user acceptance of SBC technologies. Thus, in this study, we propose a fine-tuned transformer model along with an automatic sentiment labels generation technique to perform sentiment analysis as a step towards getting insights into user acceptance of BCPs technologies. The results obtained in this study are promising; given the condition that there is no training data available from BCPs. The proposed approach was validated against IMDB reviews dataset and achieved weighted F1-score of 79% for sentiment analysis task.
... The positive impact of immigrant diversity on the GVC position of destination countries comes from the "capability pool effect", which mainly follows from the fact that immigrant diversity is positively associated with production capabilities in destination countries. Individuals with different geographical, ethnic, or cultural backgrounds have unique capabilities to perceive and solve problems [27][28][29][30][31]. Thus, immigrant diversity can bring more heterogeneous capabilities to destination countries. ...
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This paper investigates the effect of immigrant diversity on a country’s position in global value chains (GVCs) and how this effect depends on the institutional quality of destination countries. We investigate this issue using data on 19 manufacturing sectors of 18 OECD countries over the 2000–2014 period. Fixed effects estimation results show that the impact of immigrant diversity on the GVC position is significantly influenced by the institutional quality of destination countries. Specifically, in countries with high (low) institutional quality, immigrant diversity is positively (negatively) associated with the GVC position. Moreover, the interaction effect of immigrant diversity and institutional quality on the GVC position is heterogeneous across immigrant groups and institutional dimensions. This study not only enriches the literature on the relationship between immigrant diversity and GVC position but also discusses new ideas that can promote GVC positions of real economics, which is essential for sustainable economic development.
... Ferrucci & Lissoni (2019) found that the diversity of highly skilled foreigners plays an essential role in boosting creativity and technological innovations at the team level. In a similar vein, Hong & Page (2004) reveal that the demographic, cultural, ethnic, and expertise diversity of a team contribute positively to decision making and problem-solving at the workplace. Overall, the immigrants' diversity in skills and education is positively correlated with their performance in the workplace. ...
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In today's globalised world, international migration represents one of the most important topics of the 21st century, with diverse effects on a country's economic, political, and social dimensions. Depending on how well government actors succeed to manage these flows, the positive effects can be further boosted, while the adverse effects can be diminished (for both receiving and sending countries). A great deal of attention must be paid to policy strategies that foster domestic investments and innovation, as they represent a meaningful engine of economic growth, influencing positively and significantly the income in the long run. This research aims to evaluate the influence of human capital (with a focus on foreign human resources), innovation activities and investments (finance and support) on per capita economic growth (proxied by GDP per capita), in the case of all the European Union countries. The timeframe is between 2014 and 2021. For the econometric analysis of the panel data, we used Fixed-Effects regression and System GMM approach (both short-run and long-run estimations). The econometric results emphasise the positive and statistically significant effects (both on short-run and long-run) of foreign PhD students, patent applications, resource productivity, employment in innovative enterprises and tertiary educated people on per capita economic growth. The coefficients of the independent variables were higher in the long run than in the short run. Therefore, in the long run, a one standard deviation improvement in variables: foreign PhD students and patent applications lead to a 0.014-fold, respectively 0.088-fold increase in the logarithm GDP per capita.
... Across science and management fields, interdisciplinary and diverse collaboration produces stronger, more innovative outcomes (Hong & Page, 2004;Schmidt et al., 2017). This innovation can be crucial in addressing major societal challenges, such as climate change and loss of biodiversity, and may provide additional benefits, including greater citation impact (Yegros-Yegros et al., 2015), cost-sharing of resources, and simultaneous advancement across fields. ...
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For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.
... Underrepresented scholars bring myriad benefits to STEM and scientific culture. Scholars from diverse backgrounds have made countless meaningful contributions to science, increasing the novelty, impact, and quantity of scientific discoveries [10][11][12]. Furthermore, increasing the diversity of identities in STEM has positive implications for the well-being of existing diverse scholars, contributing to higher resilience, motivation, and confidence in their careers [13]. ...
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... (36)(37)(38) These effects can be powerful-a research article determined that a group of diverse problem solvers can outperform groups of high-ability problem solvers. (39) ...
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The fourth industrial revolution will likely be fueled by operationalizing biomanufacturing and engineering biology. Critical to realizing the benefits of these opportunities will be a diverse workforce and ensuring that the benefits of biotechnology will be equitably dispersed across the United States. In this policy paper, Albert Hinman discusses workforce development, resource cultivation, and open science opportunities for a more inclusive and equitable bioeconomy.
... Diversity of USGS staff at volcano observatories does not reflect wider US demographics (Bernard and Cooperdock 2018), but there has been some improvement in gender diversity, for example, in recent years (Fig. 1c). Broadly increasing diversity, equity, and engagement at US observatories, a current strategic USGS goal, will likely benefit their scientific impact and improve hazard communication (e.g., Hong and Page 2004;Maldonado et al. 2015;AlShebli et al. 2018;Nielsen et al. 2018). In the next decade, observatory participation in DEIA programs, such as the Unlearning Racism in Geoscience curriculum (https:// urgeo scien ce. ...
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... Diverse groups of people are more effective at solving complex problems than homogenous groups of high-achieving individuals (Hong and Page, 2004). As such, if we want to address our most complex biological problems, we need diverse groups of scientists working together. ...
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Instructor Talk-noncontent and nonlogistical language that is focused on shaping the classroom learning environment-is a recently defined variable that may play an important role in how undergraduates experience courses. Previous research characterized Instructor Talk used by faculty teaching in biology lecture classrooms. However, graduate teaching assistants (GTAs) and laboratory classrooms represent critical factors in undergraduate education, and Instructor Talk in this context has yet to be explored. Here, we present findings analyzing Instructor Talk used by GTAs teaching in undergraduate biology laboratory classrooms. We characterized the Instructor Talk used by 22 GTA instructors across 24 undergraduate biology laboratory courses in the context of a single, urban, Hispanic-serving and Asian American and Pacific Islander-serving Institution. We found that Instructor Talk was present in every course studied, GTAs with pedagogical training and prior teaching experience used more Instructor Talk than those without, and GTAs teaching laboratory courses used more Instructor Talk than previous observations of faculty teaching lecture courses. Given the widespread use of Instructor Talk and its varying use across contexts, we predict that Instructor Talk may be a critical variable in teaching, specifically in promoting equity and inclusion, which merits continued study in undergraduate science education.
... Collaboration will be required to tackle increasingly challenging and multidisciplinary problems. Diverse teams composed of individuals of varying perspectives and heuristic techniques often out-perform teams of high-ability problem solvers (e.g., Hong and Page 2004). Schools can conduct group exercises that require students to work together to produce an accurate and consistent forecast and/or message through consensus and working together through the scientific process. ...
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A series of webinars and panel discussions were conducted on the topic of the evolving role of humans in weather prediction and communication, in recognition of the 100th anniversary of the founding of the AMS. One main theme that arose was the inevitability that new tools using artificial intelligence will improve data analysis, forecasting, and communication. We discussed what tools are being created, how they are being created, and how the tools will potentially affect various duties for operational meteorologists in multiple sectors of the profession. Even as artificial intelligence increases automation, humans will remain a vital part of the forecast process as that process changes over time. Additionally, both university training and professional development must be revised to accommodate the evolving forecasting process, including addressing the need for computing and data skills (including artificial intelligence and visualization), probabilistic and ensemble forecasting, decision support, and communication skills. These changing skill sets necessitate that both the U.S. government’s Meteorologist General Schedule-1340 requirements and the AMS standards for a bachelor’s degree need to be revised. Seven recommendations are presented for student and forecaster preparation and career planning, highlighting the need for students and operational meteorologists to be flexible life-long learners, acquire new skills, and be engaged in the changes to forecast technology in order to best serve the user community throughout their careers. The article closes with our vision for the ways that humans can maintain an essential role in weather prediction and communication, highlighting the interdependent relationship between computers and humans.
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This chapter will review diversity, inclusion, equity, and the implementation of these constructs for training programs, faculty recruitment, and psychiatric education in order to prepare future psychiatrists to be culturally competent. In addition, the chapter will identify the importance of building allyship among trainees, culturally competent supervision for trainees, and an understanding regarding cultural humility within psychiatry training.
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Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross‐linguistic datasets following a rigorous pipeline to minimize overfitting, including cross‐validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out‐of‐sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability.
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The growth of published science in recent years has escalated the difficulty that human and algorithmic agents face in reasoning over prior knowledge to select the next experiment. This challenge is increased by uncertainty about the reproducibility of published findings. The availability of massive digital archives, machine reading, extraction tools and automated high-throughput experiments allows us to evaluate these challenges computationally at scale and identify novel opportunities to craft policies that accelerate scientific progress. Here we demonstrate a Bayesian calculus that enables positive prediction of robust scientific claims with findings extracted from published literature, weighted by scientific, social and institutional factors demonstrated to increase replicability. Illustrated with the case of gene regulatory interactions, our approach automatically estimates and counteracts sources of bias, revealing that scientifically focused but socially and institutionally diverse research activity is most likely to replicate. This results in updated certainty about the literature, which accurately predicts robust scientific facts on which new experiments should build. Our findings allow us to identify and evaluate policy recommendations for scientific institutions that may increase robust scientific knowledge, including sponsorship of increased diversity of and independence between investigations of any particular scientific phenomenon, and diversity of scientific phenomena investigated.
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International integrative research and education programs that address societal challenges such as flood risk can provide excellent out-of-class learning experiences for students by encouraging them to go beyond their own disciplines and tackle problems collaboratively with other students from various backgrounds. In recognition of the increasing importance of multidisciplinary approaches in research projects, it is worthwhile to discuss how to effectively design such integrative research and education programs to ensure successful learning outcomes for participating students. The NSF PIRE Coastal Flood Risk Reduction Program is an international place- and problem-based research education program in which students conduct case study research across the upper Texas coast in the United States and the North Sea coast in the Netherlands. Four yearly student research trips to the Netherlands were conducted from 2016 to 2019. Each year, multiple case studies (place-based) are designed for each country, covering both surge-based and precipitation-driven flood problems (problem-based). A total of 58 graduate and undergraduate students from various disciplines, including engineering, planning, economics, hydrology, biology, architecture, geography, communications, and computational hydraulics, participated. A 2-week long research trip in the Netherlands is designed to embrace the concepts of “convergence” to effectively provide students with transformative and authentic-learning experiences. This chapter describes how to design an international integrative research and education program by integrating and converging knowledge, data, and experiences across disciplines—from development to implementation. Then, it discusses reflections and lessons learned from the first 4 years (out of 7) of the program. This chapter will offer guidance to faculty, researchers, and program coordinators in higher education who desire to create a similar program.
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Science, technology, engineering, and mathematics (STEM) career barriers persist for individuals from marginalized communities due to financial and educational inequality, unconscious bias, and other disadvantaging factors. To evaluate differences in plans and interests between historically underrepresented (UR) and well-represented (WR) groups, we surveyed more than 3000 undergraduates enrolled in chemistry courses. Survey responses showed all groups arrived on campus with similar interests in learning more about science research. Over the 4 years of college, WR students maintained their interest levels, but UR students did not, creating a widening gap between the groups. Without intervention, UR students participated in lab research at lower rates than their WR peers. A case study pilot program, Biosciences Collaborative for Research Engagement (BioCoRE), encouraged STEM research exploration by undergraduates from marginalized communities. BioCoRE provided mentoring and programming that increased community cohesion and cultivated students' intrinsic scientific mindsets. Our data showed that there was no statistical significant difference between BioCoRE WR and UR students when surveyed about plans for a medical profession, graduate school, and laboratory scientific research. In addition, BioCoRE participants reported higher levels of confidence in conducting research than non-BioCoRE Scholars. We now have the highest annual number of UR students moving into PhD programs in our institution's history.
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Objective To explore challenges and opportunities for supporting midcareer women otolaryngologists in the areas of negotiation and sponsorship. Study Design Qualitative approach using semistructured interviews. Setting Online multi-institutional interviews. Methods This study was performed from June to August 2021. Women otolaryngologists representing diverse subspecialties, training, and practice environments were recruited via a purposive criterion-based sampling approach. Semistructured interviews were transcribed, coded, and analyzed via an inductive-deductive approach to produce a thematic content analysis. Results Among the 12 women interviewees, who represented 7 subspecialties, the majority were Caucasian (58%) and in academic practice (50%). The median residency graduation year was 2002 (range, 1982-2013). Participants expressed several challenges that women otolaryngologists face with respect to negotiation, including the absence of systematic formal negotiation training, gendered expectations that women experience during negotiations, and a perceived lack of power in negotiations. Obstacles to effective sponsorship included difficulty in the identification of sponsors and the influence of gender and related systemic biases that hindered sponsorship opportunities. Conclusion Notable gender disparities exist for negotiation and sponsorship in the midcareer stage for women otolaryngologists. Women start at a disadvantage due to a lack of negotiation training and access to sponsors, which is exacerbated by systemic gender bias and power differentials as women advance in their careers. This study highlights opportunities to improve negotiation and sponsorship for women, with the goal of promoting a more diverse workforce.
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We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation.
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Cognition is a central element of organizational behavior and leaders are seen as key shapers of organizational cognition. Leaders’ influence over organizations often occurs through their influence on the collectives or teams they are leading. Hence, leaders influence organizational outcomes by modeling team cognition. Despite the importance of this relationship for organizational outcomes, there is little integration currently between the leadership and team cognition literatures. To address this gap, we conduct an integrative review. First, we develop a model for leader and team influence based on organizational emergence and leadership complexity theories. Our model makes a distinction between the source of influence over cognition (leader → team, team → leader, reciprocal) and form of cognition emergence (variance reduction, variance enhancement); constraints that shape cognitions that vary in levels (within and between-level, contextual) and focus (individual, interindividual, collective); and leader behaviors (administrative, adaptive, enabling). We apply this model to review and analyze ninety-nine studies in the current literature and then discuss the limitations and future directions drawing on our findings and theoretical model. We contribute a unifying framework of leadership and team emergence that can be expanded and applied to other settings.
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Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
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Creativity doesn’t spark in a vacuum. Nurturing it with the right ingredients and trusting in the eventual payoff is what sets a creative business apart.
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We examine interpersonal congruence, the degree to which group members see others in the group as others see themselves, as a moderator of the relationship between diversity and group effectiveness. A longitudinal study of 83 work groups revealed that diversity tended to improve creative task performance in groups with high interpersonal congruence, whereas diversity undermined the performance of groups with low interpersonal congruence. This interaction effect also emerged on measures of social integration, group identification, and relationship conflict. By eliciting self-verifying appraisals, members of some groups achieved enough interpersonal congruence during their first ten minutes of interaction to benefit their group outcomes four months later. In contrast to theories of social categorization, the interpersonal congruence approach suggests that group members can achieve harmonious and effective work processes by expressing rather than suppressing the characteristics that make them unique.
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It is widely believed that a group of cooperating agents engaged in problem solving can solve a task faster than either a single agent or the same group of agents working in isolation from each other. Nevertheless, little is known about the quantitative improvements that result from cooperation. A number of experimental results are presented on constraint satisfaction that both test the predictions of a theory of cooperative problem solving and assess the value of cooperation for this class of problems. These experiments suggest an alternative methodology to existing techniques for solving constraint satisfaction problems in computer science and distributed artificial intelligence.
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The interaction process and performance of culturally homogeneous and culturally diverse groups were studied for 17 weeks. Initially, homogeneous groups scored higher on both process and performance effectiveness. Over time, both types of group showed improvement on process and performance, and the between-group differences converged. By week 17, there were no differences in process or overall performance, but the heterogeneous groups scored higher on two task measures, Implications for management and future research are given.
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Computational processes in distributed networks without global controls resemble a community of concurrent agents which, in their interactions, strategies, and competition, for resources behave like whole ecologies. This brings to mind the spontaneous appearance of organized behavior in biological and social systems, where agents can engage in cooperative strategies while working on the solution of particular problems. This paper analyzes the performance characteristics of interacting processes engaged in cooperative problem solving. It shows that for a wide class of problems, there is a highly nonlinear and universal increase in performance due to the interactions between agents. In some cases this is further enhanced by sharp phase transitions in the topological structure of the problem. These results are illustrated in the context of three prototypical search examples.
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A substantial amount of economic activity involves problem solving, yet economics has few, if any, formal models to address how agents of limited abilities find good solutions to difficult problems. In this paper, we construct a model of heterogeneous agents of bounded abilities and analyze their individual and collective performance. By heterogeneity, we mean differences in how individuals represent problems internally, their perspectives, and in the algorithms they use to generate solutions, their heuristics. We find that while a collection of bounded but diverse agents can locate optimal solutions to difficult problems, problem solving firms can exhibit arbitrary marginal returns to problem solvers and that the order that problem solvers are applied to a problem can matter, so that the standard story of decreasing returns to scale may not apply to problem solving firms. Journal of Economic Literature Classification Numbers: C6, D2.
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This essay illustrates that if Savage's small world assumption is relaxed, one can construct a theory of bounded rationality that incorporates some of the insights from recent work in cognitive psychology. The theory can be used to explain why contracts are incomplete and the existence of endowment effects in exchange. /// Décision, contrat et émotion: une économique pour un monde complexe et confus. Ce mémoire illustre le fait que si l'on relaxe le postulat du petit monde de Savage, on peut construire une théorie de la rationalité limitée qui incorpore queloques unes des perspectives en provenance des travaux récents en psychologie cognitive. La théorie peut être utilisée pour expliquer l'existence de contrats incomplets et d'effets de dotation dans l'échange.
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