Search

Your search keyword '"Shariff, Azim"' showing total 396 results
Start Over Author "Shariff, Azim"
396 results on '"Shariff, Azim"'

1. A synthesis of evidence for policy from behavioural science during COVID-19.

Author

Ruggeri, Kai, Stock, Friederike, Haslam, S, Capraro, Valerio, Boggio, Paulo, Ellemers, Naomi, Cichocka, Aleksandra, Douglas, Karen, Rand, David, van der Linden, Sander, Cikara, Mina, Finkel, Eli, Druckman, James, Wohl, Michael, Petty, Richard, Tucker, Joshua, Shariff, Azim, Gelfand, Michele, Packer, Dominic, Jetten, Jolanda, Van Lange, Paul, Pennycook, Gordon, Peters, Ellen, Baicker, Katherine, Crum, Alia, Weeden, Kim, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Skitka, Linda, Kitayama, Shinobu, Mobbs, Dean, Sunstein, Cass, Ashcroft-Jones, Sarah, Todsen, Anna, Hajian, Ali, Verra, Sanne, Buehler, Vanessa, Friedemann, Maja, Hecht, Marlene, Mobarak, Rayyan, Karakasheva, Ralitsa, Tünte, Markus, Yeung, Siu, Rosenbaum, R, Lep, Žan, Yamada, Yuki, Hudson, Sa-Kiera, Macchia, Lucía, Soboleva, Irina, Dimant, Eugen, Geiger, Sandra, Jarke, Hannes, Wingen, Tobias, Berkessel, Jana, Mareva, Silvana, McGill, Lucy, Papa, Francesca, Većkalov, Bojana, Afif, Zeina, Buabang, Eike, Landman, Marna, Tavera, Felice, Andrews, Jack, Bursalıoğlu, Aslı, Zupan, Zorana, Wagner, Lisa, Navajas, Joaquín, Vranka, Marek, Kasdan, David, Chen, Patricia, Hudson, Kathleen, Novak, Lindsay, Teas, Paul, Rachev, Nikolay, Galizzi, Matteo, Milkman, Katherine, Petrović, Marija, Van Bavel, Jay, and Willer, Robb

Subjects
Humans, Behavioral Sciences, Communication, COVID-19, Culture, Evidence-Based Practice, Health Policy, Leadership, Pandemics, Policy Making, Public Health, Social Norms
Abstract

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (claims) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms physical distancing and social distancing. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Published
2024

3. Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation

Author

Awad, Edmond, Levine, Sydney, Kleiman-Weiner, Max, Dsouza, Sohan, Tenenbaum, Joshua B., Shariff, Azim, Bonnefon, Jean-François, and Rahwan, Iyad

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

When a semi-autonomous car crashes and harms someone, how are blame and causal responsibility distributed across the human and machine drivers? In this article, we consider cases in which a pedestrian was hit and killed by a car being operated under shared control of a primary and a secondary driver. We find that when only one driver makes an error, that driver receives the blame and is considered causally responsible for the harm, regardless of whether that driver is a machine or a human. However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced. This finding portends a public under-reaction to the malfunctioning AI components of semi-autonomous cars and therefore has a direct policy implication: a bottom-up regulatory scheme (which operates through tort law that is adjudicated through the jury system) could fail to properly regulate the safety of shared-control vehicles; instead, a top-down scheme (enacted through federal laws) may be called for.

Published
2018

4. Cooperating with Machines

Author

Crandall, Jacob W., Oudah, Mayada, Tennom, Ishowo-Oloko, Fatimah, Abdallah, Sherief, Bonnefon, Jean-François, Cebrian, Manuel, Shariff, Azim, Goodrich, Michael A., and Rahwan, Iyad

Subjects
Computer Science - Artificial Intelligence
Abstract

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms., Comment: An updated version of this paper was published in Nature Communications

Published
2017
Full Text
View/download PDF

11. A synthesis of evidence for policy from behavioural science during COVID-19

Author

Ruggeri, Kai; https://orcid.org/0000-0002-8470-101X, Stock, Friederike, Haslam, S Alexander; https://orcid.org/0000-0001-9523-7921, Capraro, Valerio; https://orcid.org/0000-0002-0579-0166, Boggio, Paulo, Ellemers, Naomi; https://orcid.org/0000-0001-9810-1165, Cichocka, Aleksandra, Douglas, Karen M; https://orcid.org/0000-0002-0381-6924, Rand, David G; https://orcid.org/0000-0001-8975-2783, van der Linden, Sander; https://orcid.org/0000-0002-0269-1744, Cikara, Mina; https://orcid.org/0000-0002-6612-4474, Finkel, Eli J; https://orcid.org/0000-0002-0213-5318, Druckman, James N; https://orcid.org/0000-0002-1249-6790, Wohl, Michael J A; https://orcid.org/0000-0001-6945-5562, Petty, Richard E; https://orcid.org/0000-0002-2870-8575, Tucker, Joshua A; https://orcid.org/0000-0003-1321-8650, Shariff, Azim; https://orcid.org/0000-0003-4444-460X, Gelfand, Michele, Packer, Dominic; https://orcid.org/0000-0001-8420-6548, Jetten, Jolanda, Van Lange, Paul A M; https://orcid.org/0000-0001-7774-6984, Pennycook, Gordon, Peters, Ellen; https://orcid.org/0000-0003-0702-6169, Baicker, Katherine, Crum, Alia, Weeden, Kim A; https://orcid.org/0000-0002-9975-8457, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Većkalov, Bojana; https://orcid.org/0000-0002-8477-1261, Wagner, Lisa; https://orcid.org/0000-0002-1925-2676, et al, Ruggeri, Kai; https://orcid.org/0000-0002-8470-101X, Stock, Friederike, Haslam, S Alexander; https://orcid.org/0000-0001-9523-7921, Capraro, Valerio; https://orcid.org/0000-0002-0579-0166, Boggio, Paulo, Ellemers, Naomi; https://orcid.org/0000-0001-9810-1165, Cichocka, Aleksandra, Douglas, Karen M; https://orcid.org/0000-0002-0381-6924, Rand, David G; https://orcid.org/0000-0001-8975-2783, van der Linden, Sander; https://orcid.org/0000-0002-0269-1744, Cikara, Mina; https://orcid.org/0000-0002-6612-4474, Finkel, Eli J; https://orcid.org/0000-0002-0213-5318, Druckman, James N; https://orcid.org/0000-0002-1249-6790, Wohl, Michael J A; https://orcid.org/0000-0001-6945-5562, Petty, Richard E; https://orcid.org/0000-0002-2870-8575, Tucker, Joshua A; https://orcid.org/0000-0003-1321-8650, Shariff, Azim; https://orcid.org/0000-0003-4444-460X, Gelfand, Michele, Packer, Dominic; https://orcid.org/0000-0001-8420-6548, Jetten, Jolanda, Van Lange, Paul A M; https://orcid.org/0000-0001-7774-6984, Pennycook, Gordon, Peters, Ellen; https://orcid.org/0000-0003-0702-6169, Baicker, Katherine, Crum, Alia, Weeden, Kim A; https://orcid.org/0000-0002-9975-8457, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Većkalov, Bojana; https://orcid.org/0000-0002-8477-1261, Wagner, Lisa; https://orcid.org/0000-0002-1925-2676, and et al

Abstract

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Published
2024

12. The social dilemma of autonomous vehicles

Author

Bonnefon, Jean-François, Shariff, Azim, and Rahwan, Iyad

Subjects
Computer Science - Computers and Society
Abstract

Autonomous Vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils-for example, running over pedestrians or sacrificing itself and its passenger to save them. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants to six MTurk studies approved of utilitarian AVs (that sacrifice their passengers for the greater good), and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. They would disapprove of enforcing utilitarian AVs, and would be less willing to buy such a regulated AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology., Comment: 14 pages, 3 figures

Published
2015
Full Text
View/download PDF

14. Why do organizations take political stances? A review of reasons and risks.

Author

Clark, Cory J., Isch, Calvin, and Shariff, Azim

Subjects
POLITICAL organizations, POLITICAL participation, TRUST, POLITICAL endorsements, POLITICIANS, BOYCOTTS, POLITICAL trust (in government)
Abstract

Organizations and their leaders have begun publicly signaling political values in candidate endorsements, statements, and advertisements, yet political action often has negative organizational consequences, including lower public support, financial costs, and reduced trust. We review the costs of organizational politicization, moderators of those costs (such as ideological alignment and size of the organization), and potential reasons why leaders take political action. Scholars often attribute political action to public pressure to "take a stand", but this public pressure may be misunderstood. Members of the public who want organizations to take political stances desire particular stances to be made in particular ways, tend to believe in the superiority of their own values, and are relatively likely to boycott businesses for political reasons. Catering to these individuals could lead to the accumulation of supporters who are especially politically zealous and likely to punish perceived political missteps. Demands to "take a stand" might seem like one unified call to action, but they may instead be a large set of directly conflicting demands. We make recommendations for future research to better understand leaders' reasons for political action and when, if ever, such actions support the interests of organizations and broader society. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

15. The social dilemma of autonomous vehicles

Author

Bonnefon, Jean-François, Shariff, Azim, and Rahwan, Iyad

Subjects
Good Health and Well Being, Accidents, Traffic, Humans, Morals, Motor Vehicles, Safety, General Science & Technology
Abstract

Autonomous vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants in six Amazon Mechanical Turk studies approved of utilitarian AVs (that is, AVs that sacrifice their passengers for the greater good) and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. The study participants disapprove of enforcing utilitarian regulations for AVs and would be less willing to buy such an AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology.

Published
2016

16. A synthesis of evidence for policy from behavioural science during COVID-19

Author

Ruggeri, Kai, primary, Stock, Friederike, additional, Haslam, S. Alexander, additional, Capraro, Valerio, additional, Boggio, Paulo, additional, Ellemers, Naomi, additional, Cichocka, Aleksandra, additional, Douglas, Karen M., additional, Rand, David G., additional, van der Linden, Sander, additional, Cikara, Mina, additional, Finkel, Eli J., additional, Druckman, James N., additional, Wohl, Michael J. A., additional, Petty, Richard E., additional, Tucker, Joshua A., additional, Shariff, Azim, additional, Gelfand, Michele, additional, Packer, Dominic, additional, Jetten, Jolanda, additional, Van Lange, Paul A. M., additional, Pennycook, Gordon, additional, Peters, Ellen, additional, Baicker, Katherine, additional, Crum, Alia, additional, Weeden, Kim A., additional, Napper, Lucy, additional, Tabri, Nassim, additional, Zaki, Jamil, additional, Skitka, Linda, additional, Kitayama, Shinobu, additional, Mobbs, Dean, additional, Sunstein, Cass R., additional, Ashcroft-Jones, Sarah, additional, Todsen, Anna Louise, additional, Hajian, Ali, additional, Verra, Sanne, additional, Buehler, Vanessa, additional, Friedemann, Maja, additional, Hecht, Marlene, additional, Mobarak, Rayyan S., additional, Karakasheva, Ralitsa, additional, Tünte, Markus R., additional, Yeung, Siu Kit, additional, Rosenbaum, R. Shayna, additional, Lep, Žan, additional, Yamada, Yuki, additional, Hudson, Sa-kiera Tiarra Jolynn, additional, Macchia, Lucía, additional, Soboleva, Irina, additional, Dimant, Eugen, additional, Geiger, Sandra J., additional, Jarke, Hannes, additional, Wingen, Tobias, additional, Berkessel, Jana B., additional, Mareva, Silvana, additional, McGill, Lucy, additional, Papa, Francesca, additional, Većkalov, Bojana, additional, Afif, Zeina, additional, Buabang, Eike K., additional, Landman, Marna, additional, Tavera, Felice, additional, Andrews, Jack L., additional, Bursalıoğlu, Aslı, additional, Zupan, Zorana, additional, Wagner, Lisa, additional, Navajas, Joaquín, additional, Vranka, Marek, additional, Kasdan, David, additional, Chen, Patricia, additional, Hudson, Kathleen R., additional, Novak, Lindsay M., additional, Teas, Paul, additional, Rachev, Nikolay R., additional, Galizzi, Matteo M., additional, Milkman, Katherine L., additional, Petrović, Marija, additional, Van Bavel, Jay J., additional, and Willer, Robb, additional

Published
2023
Full Text
View/download PDF

17. Analytical reasoning task reveals limits of social learning in networks

Author

Rahwan, Iyad, Krasnoshtan, Dmytro, Shariff, Azim, and Bonnefon, Jean-Francois

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias,' which limits their social learning to the output, rather than the process, of their peers' reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Published
2014
Full Text
View/download PDF

19. Using social and behavioural science to support COVID-19 pandemic response

Author

Bavel, Jay J. Van, Baicker, Katherine, Boggio, Paulo S., Capraro, Valerio, Cichocka, Aleksandra, Cikara, Mina, Crockett, Molly J., Crum, Alia J., Douglas, Karen M., Druckman, James N., Drury, John, Dube, Oeindrila, Ellemers, Naomi, Finkel, Eli J., Fowler, James H., Gelfand, Michele, Han, Shihui, Haslam, S. Alexander, Jetten, Jolanda, Kitayama, Shinobu, Mobbs, Dean, Napper, Lucy E., Packer, Dominic J., Pennycook, Gordon, Peters, Ellen, Petty, Richard E., Rand, David G., Reicher, Stephen D., Schnall, Simone, Shariff, Azim, Skitka, Linda J., Smith, Sandra Susan, Sunstein, Cass R., Tabri, Nassim, Tucker, Joshua A., Linden, Sander van der, Lange, Paul van, Weeden, Kim A., Wohl, Michael J. A., Zaki, Jamil, Zion, Sean R., and Willer, Robb

Published
2020
Full Text
View/download PDF

30. Machine behaviour

Author

Rahwan, Iyad, Cebrian, Manuel, Obradovich, Nick, Bongard, Josh, Bonnefon, Jean-François, Breazeal, Cynthia, Crandall, Jacob W., Christakis, Nicholas A., Couzin, Iain D., Jackson, Matthew O., Jennings, Nicholas R., Kamar, Ece, Kloumann, Isabel M., Larochelle, Hugo, Lazer, David, McElreath, Richard, Mislove, Alan, Parkes, David C., Pentland, Alex ‘Sandy’, Roberts, Margaret E., Shariff, Azim, Tenenbaum, Joshua B., and Wellman, Michael

Published
2019
Full Text
View/download PDF

34. The Moral Psychology of Artificial Intelligence.

Author

Bonnefon, Jean-François, Rahwan, Iyad, and Shariff, Azim

Subjects
EVALUATION of medical care, ETHICS, USER interfaces, ARTIFICIAL intelligence, PSYCHOLOGY, MACHINERY, COOPERATIVENESS, HUMANISM, DECISION making, COMMUNICATION, SUPERVISION of employees, PROXY, ALGORITHMS
Abstract

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in artificial intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients or solving moral dilemmas without human supervision. Machines can be perceived as moral patients, whose outcomes can be affected by human decisions, with important consequences for human–machine cooperation. Machines can be moral proxies that human agents and patients send as their delegates to moral interactions or use as a disguise in these interactions. Here we review the experimental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

42. Megastudy identifying effective interventions to strengthen Americans’ democratic attitudes

Author

Voelkel, Jan G., primary, Stagnaro, Michael, additional, Chu, James, additional, Pink, Sophia Lerner, additional, Mernyk, Joseph S., additional, Redekopp, Chrystal, additional, Ghezae, Isaias, additional, Cashman, Matthew, additional, Adjodah, Dhaval, additional, Allen, Levi, additional, Allis, Victor, additional, Baleria, Gina, additional, Ballantyne, Nathan, additional, Van Bavel, Jay Joseph, additional, Blunden, Hayley, additional, Braley, Alia, additional, Bryan, Christopher, additional, Celniker, Jared, additional, Cikara, Mina, additional, Clapper, Margarett, additional, Clayton, Katherine, additional, Collins, Hanne K., additional, DeFilippis, Evan, additional, DIEFFENBACH, MACRINA, additional, Doell, Kimberly C, additional, Dorison, Charles, additional, Duong, Mylien, additional, Felsman, Peter, additional, Fiorella, Maya, additional, Francis, David, additional, Franz, Michael M., additional, Gallardo, Roman, additional, Gifford, Sara, additional, Goya-Tocchetto, Daniela, additional, Gray, Kurt, additional, Green, Joe, additional, Greene, Joshua D., additional, Güngör, Mertcan, additional, Hall, Matthew, additional, Hecht, Cameron, additional, Javeed, Ali, additional, Jost, John, additional, Kay, Aaron, additional, Kay, Nicholas, additional, Keating, Brandyn, additional, Kelly, John Michael, additional, Kirk, James, additional, Kopell, Malka, additional, Kteily, Nour, additional, Kubin, Emily, additional, Lees, Jeffrey Martin, additional, Lenz, Gabriel, additional, Levendusky, Matt, additional, Littman, Rebecca, additional, Luo, Kara, additional, Lyles, Aaron, additional, Lyons, Benjamin, additional, Marsh, Wayde, additional, Martherus, James, additional, Maurer, Lauren Alpert, additional, Mehl, Caroline, additional, minson, julia, additional, Moore, Molly, additional, Moore-Berg, Samantha, additional, Pasek, Michael H., additional, Pentland, Alex, additional, Puryear, Curtis, additional, Rahnama, Hossein, additional, Rathje, Steve, additional, Rosato, Jay, additional, Saar-Tsechansky, Maytal, additional, Santos, Luiza, additional, Seifert, Colleen, additional, Shariff, Azim, additional, Simonsson, Otto, additional, Spitz Siddiqi, Shiri, additional, Stone, Daniel, additional, Strand, Palma, additional, Tomz, Michael, additional, Yeager, David Scott, additional, Yoeli, Erez, additional, zaki, jamil, additional, Druckman, James, additional, Rand, David Gertler, additional, and Willer, Robb, additional

Published
2023
Full Text
View/download PDF

43. A synthesis of evidence for policy from behavioural science during COVID-19

Author

Leerstoel Ellemers, Social identity: Morality and diversity, Leerstoel de Wit, Social Policy and Public Health, Ruggeri, Kai, Stock, Friederike, Haslam, S Alexander, Capraro, Valerio, Boggio, Paulo, Ellemers, Naomi, Cichocka, Aleksandra, Douglas, Karen M, Rand, David G, van der Linden, Sander, Cikara, Mina, Finkel, Eli J, Druckman, James N, Wohl, Michael J A, Petty, Richard E, Tucker, Joshua A, Shariff, Azim, Gelfand, Michele, Packer, Dominic, Jetten, Jolanda, Van Lange, Paul A M, Pennycook, Gordon, Peters, Ellen, Baicker, Katherine, Crum, Alia, Weeden, Kim A, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Skitka, Linda, Kitayama, Shinobu, Mobbs, Dean, Sunstein, Cass R, Ashcroft-Jones, Sarah, Todsen, Anna Louise, Hajian, Ali, Verra, Sanne, Buehler, Vanessa, Friedemann, Maja, Hecht, Marlene, Mobarak, Rayyan S, Karakasheva, Ralitsa, Tünte, Markus R, Yeung, Siu Kit, Rosenbaum, R Shayna, Lep, Žan, Yamada, Yuki, Hudson, Sa-Kiera Tiarra Jolynn, Macchia, Lucía, Soboleva, Irina, Dimant, Eugen, Geiger, Sandra J, Jarke, Hannes, Wingen, Tobias, Berkessel, Jana B, Mareva, Silvana, McGill, Lucy, Papa, Francesca, Većkalov, Bojana, Afif, Zeina, Buabang, Eike K, Landman, Marna, Tavera, Felice, Andrews, Jack L, Bursalıoğlu, Aslı, Zupan, Zorana, Wagner, Lisa, Navajas, Joaquín, Vranka, Marek, Kasdan, David, Chen, Patricia, Hudson, Kathleen R, Novak, Lindsay M, Teas, Paul, Rachev, Nikolay R, Galizzi, Matteo M, Milkman, Katherine L, Petrović, Marija, Van Bavel, Jay J, Willer, Robb, Leerstoel Ellemers, Social identity: Morality and diversity, Leerstoel de Wit, Social Policy and Public Health, Ruggeri, Kai, Stock, Friederike, Haslam, S Alexander, Capraro, Valerio, Boggio, Paulo, Ellemers, Naomi, Cichocka, Aleksandra, Douglas, Karen M, Rand, David G, van der Linden, Sander, Cikara, Mina, Finkel, Eli J, Druckman, James N, Wohl, Michael J A, Petty, Richard E, Tucker, Joshua A, Shariff, Azim, Gelfand, Michele, Packer, Dominic, Jetten, Jolanda, Van Lange, Paul A M, Pennycook, Gordon, Peters, Ellen, Baicker, Katherine, Crum, Alia, Weeden, Kim A, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Skitka, Linda, Kitayama, Shinobu, Mobbs, Dean, Sunstein, Cass R, Ashcroft-Jones, Sarah, Todsen, Anna Louise, Hajian, Ali, Verra, Sanne, Buehler, Vanessa, Friedemann, Maja, Hecht, Marlene, Mobarak, Rayyan S, Karakasheva, Ralitsa, Tünte, Markus R, Yeung, Siu Kit, Rosenbaum, R Shayna, Lep, Žan, Yamada, Yuki, Hudson, Sa-Kiera Tiarra Jolynn, Macchia, Lucía, Soboleva, Irina, Dimant, Eugen, Geiger, Sandra J, Jarke, Hannes, Wingen, Tobias, Berkessel, Jana B, Mareva, Silvana, McGill, Lucy, Papa, Francesca, Većkalov, Bojana, Afif, Zeina, Buabang, Eike K, Landman, Marna, Tavera, Felice, Andrews, Jack L, Bursalıoğlu, Aslı, Zupan, Zorana, Wagner, Lisa, Navajas, Joaquín, Vranka, Marek, Kasdan, David, Chen, Patricia, Hudson, Kathleen R, Novak, Lindsay M, Teas, Paul, Rachev, Nikolay R, Galizzi, Matteo M, Milkman, Katherine L, Petrović, Marija, Van Bavel, Jay J, and Willer, Robb

Published
2023

44. A synthesis of evidence for policy from behavioural science during COVID-19

Author

Ruggeri, Kai, Stock, Friederike, Haslam, Alexander S., Capraro, Valerio, Boggio, P., Ellemers, N., Cichocka, A., Douglas, Karen M., Rand, David G., van der Linden, Sander, Cikara, Mina, Finkel, Eli J., Druckman, James N., Wohl, Michael J.A., Petty, Richard E., Tucker, Joshua A., Shariff, Azim, Gelfand, M., Packer, Dominic, Jetten, Jolanda, Van Lange, Paul AM, Pennycook, Gordon, Peters, Ellen, Baicker, Katherine, Crum, Alia, Weeden, Kim A, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Skitka, Linda, Kitayama, Shinobu, Mobbs, Dean, Sunstein, Cass R, Ashcroft Jones, Sarah, Todsen, Anna Louise, Hajian, Ali, Verra, Sanne, Buehler, Vanessa, Friedemann, Maja, Hecht, Marlene, Mobarak, Rayyan S, Karakasheva, Ralitsa, Tunte, Markus R, Yeung, Siu Kit, Rosenbaum, R Shayna, Lep, Žan, Yamada, Yuki, Hudson, Sa-Kiera Tiarra Jolynn, Macchia, Lucia, Soboleva, Irina, Dimant, Eugen, Geiger, Sandra J., Jarke, Hannes, Wingen, Tobias, Berkessel, Jana B, Mareva, Silvana, McGill, Lucy, Papa, Francesca, Veckalov, Bojana, Afif, Zeina, Buabang, Eike K, Landman, Marna, Tavera, Felice, Andrews, Jack L., Bursalioglu, Asli, Zupan, Zorana, Wagner, Lisa, Navajas, Joaquin, Vranka, Marek, Kasdan, David, Chen, Patricia, Hudson, Kathleen R, Novak, Lindsay M, Teas, Paul, Rachev, Nikolay R, Galizzi, Matteo M, Milknan, Katherine L, Petrović, Marija, van Bavel, Jay J, Willer, Robb, Ruggeri, Kai, Stock, Friederike, Haslam, Alexander S., Capraro, Valerio, Boggio, P., Ellemers, N., Cichocka, A., Douglas, Karen M., Rand, David G., van der Linden, Sander, Cikara, Mina, Finkel, Eli J., Druckman, James N., Wohl, Michael J.A., Petty, Richard E., Tucker, Joshua A., Shariff, Azim, Gelfand, M., Packer, Dominic, Jetten, Jolanda, Van Lange, Paul AM, Pennycook, Gordon, Peters, Ellen, Baicker, Katherine, Crum, Alia, Weeden, Kim A, Napper, Lucy, Tabri, Nassim, Zaki, Jamil, Skitka, Linda, Kitayama, Shinobu, Mobbs, Dean, Sunstein, Cass R, Ashcroft Jones, Sarah, Todsen, Anna Louise, Hajian, Ali, Verra, Sanne, Buehler, Vanessa, Friedemann, Maja, Hecht, Marlene, Mobarak, Rayyan S, Karakasheva, Ralitsa, Tunte, Markus R, Yeung, Siu Kit, Rosenbaum, R Shayna, Lep, Žan, Yamada, Yuki, Hudson, Sa-Kiera Tiarra Jolynn, Macchia, Lucia, Soboleva, Irina, Dimant, Eugen, Geiger, Sandra J., Jarke, Hannes, Wingen, Tobias, Berkessel, Jana B, Mareva, Silvana, McGill, Lucy, Papa, Francesca, Veckalov, Bojana, Afif, Zeina, Buabang, Eike K, Landman, Marna, Tavera, Felice, Andrews, Jack L., Bursalioglu, Asli, Zupan, Zorana, Wagner, Lisa, Navajas, Joaquin, Vranka, Marek, Kasdan, David, Chen, Patricia, Hudson, Kathleen R, Novak, Lindsay M, Teas, Paul, Rachev, Nikolay R, Galizzi, Matteo M, Milknan, Katherine L, Petrović, Marija, van Bavel, Jay J, and Willer, Robb

Abstract

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Published
2023

47. Ideology trumps self-interest: continued support for a political leader despite disappointing tax returns.

Author

Rathje, Steve, Shariff, Azim, and Schnall, Simone

Subjects
*TAX returns, *POLITICIANS, *TAX refunds, *SELF-interest, *SATISFACTION
Abstract

People presumably strive to maximize their own benefit whenever possible, so it is puzzling when they vote for leaders who may not have their best interest at heart. We tested whether support for a political leader is diminished when supporters learn they are financially disadvantaged by the leader's policies. In a two-stage experiment (Time 1 n = 601, Time 2 n = 343) with pre-registered hypotheses, Trump voters predicted their expected tax refund (or payment), and then reported their tax outcome immediately after the filing deadline. Afterwards, we confronted half of the participants with the discrepancy between their actual and predicted tax outcome. Having lower-than-expected tax outcomes was not associated with reduced support for Trump either on its own, or in combination with being reminded of this outcome. However, it led participants who were dissatisfied with their tax outcome to downgrade the importance of lowering taxes, possibly in an effort to reduce dissonance and justify continued support for Trump. Subjective tax outcome satisfaction did predict Trump support, but was dwarfed in magnitude by other variables such as system justification and political orientation. Thus, people may find ways to rationalize information that goes against their self-interest into their partisan world-view. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

48. The moralization of effort.

Author

Celniker, Jared B., primary, Gregory, Andrew, additional, Koo, Hyunjin J., additional, Piff, Paul K., additional, Ditto, Peter H., additional, and Shariff, Azim F., additional

Published
2023
Full Text
View/download PDF

50. sj-docx-1-pss-10.1177_09567976231158576 – Supplemental material for Thinking About God Encourages Prosociality Toward Religious Outgroups: A Cross-Cultural Investigation

Author

Pasek, Michael H., Kelly, John Michael, Shackleford, Crystal, White, Cindel J. M., Vishkin, Allon, Smith, Julia M., Norenzayan, Ara, Shariff, Azim, and Ginges, Jeremy

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy), 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-docx-1-pss-10.1177_09567976231158576 for Thinking About God Encourages Prosociality Toward Religious Outgroups: A Cross-Cultural Investigation by Michael H. Pasek, John Michael Kelly, Crystal Shackleford, Cindel J. M. White, Allon Vishkin, Julia M. Smith, Ara Norenzayan, Azim Shariff and Jeremy Ginges in Psychological Science

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results