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27 results on '"Della Penna, Nicolas"'

1. Natural Language Mechanisms via Self-Resolution with Foundation Models

Author

Della Penna, Nicolas

Subjects
Computer Science - Computer Science and Game Theory
Abstract

Practical mechanisms often limit agent reports to constrained formats like trades or orderings, potentially limiting the information agents can express. We propose a novel class of mechanisms that elicit agent reports in natural language and leverage the world-modeling capabilities of large language models (LLMs) to select outcomes and assign payoffs. We identify sufficient conditions for these mechanisms to be incentive-compatible and efficient as the LLM being a good enough world model and a strong inter-agent information over-determination condition. We show situations where these LM-based mechanisms can successfully aggregate information in signal structures on which prediction markets fail., Comment: presented as a poster at ACM EC 24 Foundation Models and Game Theory Workshop

Published
2024

2. Towards Optimal Prior-Free Permissionless Rebate Mechanisms, with applications to Automated Market Makers & Combinatorial Orderflow Auctions

Author

Mazorra, Bruno and Della Penna, Nicolás

Subjects
Computer Science - Computer Science and Game Theory
Abstract

Maximal Extractable Value (MEV) has become a critical issue for blockchain ecosystems, as it enables validators or block proposers to extract value by ordering, including or censoring users' transactions. This paper aims to present a formal approach for determining the appropriate compensation for users whose transactions are executed in bundles, as opposed to individually. We explore the impact of MEV on users, discuss the Shapley value as a solution for fair compensation, and delve into the mechanisms of MEV rebates and auctions as a means to undermine the power of the block producer.

Published
2023

3. The Cost of Sybils, Credible Commitments, and False-Name Proof Mechanisms

Author

Mazorra, Bruno and Della Penna, Nicolás

Subjects
Computer Science - Computer Science and Game Theory
Abstract

Consider a mechanism that cannot observe how many players there are directly, but instead must rely on their self-reports to know how many are participating. Suppose the players can create new identities to report to the auctioneer at some cost $c$. The usual mechanism design paradigm is equivalent to implicitly assuming that $c$ is infinity for all players, while the usual Sybil attacks literature is that it is zero or finite for one player (the attacker) and infinity for everyone else (the 'honest' players). The false-name proof literature largely assumes the cost to be 0. We consider a model with variable costs that unifies these disparate streams. A paradigmatic normal form game can be extended into a Sybil game by having the action space by the product of the feasible set of identities to create action where each player chooses how many players to present as in the game and their actions in the original normal form game. A mechanism is (dominant) false-name proof if it is (dominant) incentive-compatible for all the players to self-report as at most one identity. We study mechanisms proposed in the literature motivated by settings where anonymity and self-identification are the norms, and show conditions under which they are not Sybil-proof. We characterize a class of dominant Sybil-proof mechanisms for reward sharing and show that they achieve the efficiency upper bound. We consider the extension when agents can credibly commit to the strategy of their sybils and show how this can break mechanisms that would otherwise be false-name proof.

Published
2023

4. Constant Function Market Making, Social Welfare and Maximal Extractable Value

Author

Mazorra, Bruno and Della Penna, Nicolás

Subjects
Computer Science - Computer Science and Game Theory, Mathematics - Probability
Abstract

We consider the social welfare that can be facilitated by a constant function market maker (CFMM). When there is sufficient liquidity available to the CFMM, it can approximate the optimal social welfare when all users transactions are executed. When one of the agent has the role of proposing the block, and blockspace is scarce, they can obtain higher expected utility than otherwise identical agents. This gives a lower bound on the maximal extractable value exposed when blockspace is scarce.

Published
2022

5. Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy

Author

Reusche, Daniel and Della Penna, Nicolás

Subjects
Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning
Abstract

Designing truthful, revenue maximizing auctions is a core problem of auction design. Multi-item settings have long been elusive. Recent work (arXiv:1706.03459) introduces effective deep learning techniques to find such auctions for the prior-dependent setting, in which distributions about bidder preferences are known. One remaining problem is to obtain priors in a way that excludes the possibility of manipulating the resulting auctions. Using techniques from differential privacy for the construction of approximately truthful mechanisms, we modify the RegretNet approach to be applicable to the prior-free setting. In this more general setting, no distributional information is assumed, but we trade this property for worse performance. We present preliminary empirical results and qualitative analysis for this work in progress.

Published
2021

6. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Cramer, Estee Y., Ray, Evan L., Lopez, Velma K., Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, Gneiting, Tilmann, House, Katie H., Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H., Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W., Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F., Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Srivastava, Ajitesh, George, Glover E., Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M., Baer, Thomas M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V. P., Guertin, Stephanie L., Hulme-Lowe, Christopher, Turner, Stephen D., Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel, Turtle, James A., Ben-Nun, Michal, Riley, Steven, Riley, Pete, Koyluoglu, Ugur, DesRoches, David, Forli, Pedro, Hamory, Bruce, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Kraus, David, Kraus, Andrea, Gao, Zhifeng, Bian, Jiang, Cao, Wei, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Vespignani, Alessandro, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore y, Xiong, Xinyue, Zheng, Andrew, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Nze-Ndong, David, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Sarker, Arnab, Jadbabaie, Ali, Shah, Devavrat, Della Penna, Nicolas, Celi, Leo A., Sundar, Saketh, Wolfinger, Russ, Osthus, Dave, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Karlen, Dean, Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Lee, Elizabeth C., Dent, Juan, Grantz, Kyra H., Hill, Alison L., Kaminsky, Joshua, Kaminsky, Kathryn, Keegan, Lindsay T., Lauer, Stephen A., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Marshall, Maximilian, Gardner, Lauren, Nixon, Kristen, Burant, John C., Wang, Lily, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Guannan, Wang, Yueying, Yu, Shan, Reiner, Robert C., Barber, Ryan, Gakidou, Emmanuela, Hay, Simon I., Lim, Steve, Murray, Chris, Pigott, David, Gurung, Heidi L., Baccam, Prasith, Stage, Steven A., Suchoski, Bradley T., Prakash, B. Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Keskinocak, Pinar, Asplund, John, Baxter, Arden, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sava, Dario, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Zhou, Mingyuan, Kalantari, Rahi, Yamana, Teresa K., Pei, Sen, Shaman, Jeffrey, Li, Michael L., Bertsimas, Dimitris, Lami, Omar Skali, Soni, Saksham, Bouardi, Hamza Tazi, Ayer, Turgay, Adee, Madeline, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Wang, Yuanjia, Wang, Qinxia, Xie, Shanghong, Zeng, Donglin, Green, Alden, Bien, Jacob, Brooks, Logan, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, O’Dea, Eamon B., Drake, John M., Pagano, Robert, Tran, Quoc T., Ho, Lam Si Tung, Huynh, Huong, Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., Biggerstaff, Matthew, and Reich, Nicholas G.

Published
2022

7. An Experimental Study of Cryptocurrency Market Dynamics

Author

Krafft, Peter M, Della Penna, Nicolás, and Pentland, Alex

Subjects
Computer Science - Computers and Society, Computer Science - Human-Computer Interaction, Computer Science - Social and Information Networks, Physics - Physics and Society, J.4, K.4.2, H.5.m
Abstract

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions., Comment: CHI 2018

Published
2018
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8. Human collective intelligence as distributed Bayesian inference

Author

Krafft, Peter M., Zheng, Julia, Pan, Wei, Della Penna, Nicolás, Altshuler, Yaniv, Shmueli, Erez, Tenenbaum, Joshua B., and Pentland, Alex

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory, Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups of fallible individuals? Answering this question requires a multiscale analysis. We must understand both the individual decision mechanisms people use, and the properties and dynamics of those mechanisms in the aggregate. As of yet, mathematical tools for such an approach have been lacking. To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference. Distributed inference occurs through information processing at the individual level, and yields rational belief formation at the group level. We instantiate this framework in a new model of human social decision-making, which we validate using a dataset we collected of over 50,000 users of an online social trading platform where investors mimic each others' trades using real money in foreign exchange and other asset markets. We find that in this setting people use a decision mechanism in which popularity is treated as a prior distribution for which decisions are best to make. This mechanism is boundedly rational at the individual level, but we prove that in the aggregate implements a type of approximate "Thompson sampling"---a well-known and highly effective single-agent Bayesian machine learning algorithm for sequential decision-making. The perspective of distributed Bayesian inference therefore reveals how collective rationality emerges from the boundedly rational decision mechanisms people use.

Published
2016

9. Compliance-Aware Bandits

Author

Della Penna, Nicolás, Reid, Mark D., and Balduzzi, David

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

Motivated by clinical trials, we study bandits with observable non-compliance. At each step, the learner chooses an arm, after, instead of observing only the reward, it also observes the action that took place. We show that such noncompliance can be helpful or hurtful to the learner in general. Unfortunately, naively incorporating compliance information into bandit algorithms loses guarantees on sublinear regret. We present hybrid algorithms that maintain regret bounds up to a multiplicative factor and can incorporate compliance information. Simulations based on real data from the International Stoke Trial show the practical potential of these algorithms.

Published
2016

10. Popularity and Performance: A Large-Scale Study

Author

Krafft, Peter, Zheng, Julia, Shmueli, Erez, Della Penna, Nicolás, Tenenbaum, Josh, and Pentland, Sandy

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

Social scientists have long sought to understand why certain people, items, or options become more popular than others. One seemingly intuitive theory is that inherent value drives popularity. An alternative theory claims that popularity is driven by the rich-get-richer effect of cumulative advantage---certain options become more popular, not because they are higher quality, but because they are already relatively popular. Realistically, it seems likely that popularity is driven by neither one of these forces alone but rather both together. Recently, researchers have begun using large-scale online experiments to study the effect of cumulative advantage in realistic scenarios, but there have been no large-scale studies of the combination of these two effects. We are interested in studying a case where decision-makers observe explicit signals of both the popularity and the quality of various options. We derive a model for change in popularity as a function of past popularity and past perceived quality. Our model implies that we should expect an interaction between these two forces---popularity should amplify the effect of quality, so that the more popular an option is, the faster we expect it to increase in popularity with better perceived quality. We use a data set from eToro.com, an online social investment platform, to support this hypothesis.

Published
2014

11. Crowd & Prejudice: An Impossibility Theorem for Crowd Labelling without a Gold Standard

Author

Della Penna, Nicolás and Reid, Mark D.

Subjects
Computer Science - Social and Information Networks, Computer Science - Computer Science and Game Theory
Abstract

A common use of crowd sourcing is to obtain labels for a dataset. Several algorithms have been proposed to identify uninformative members of the crowd so that their labels can be disregarded and the cost of paying them avoided. One common motivation of these algorithms is to try and do without any initial set of trusted labeled data. We analyse this class of algorithms as mechanisms in a game-theoretic setting to understand the incentives they create for workers. We find an impossibility result that without any ground truth, and when workers have access to commonly shared 'prejudices' upon which they agree but are not informative of true labels, there is always equilibria where all agents report the prejudice. A small amount amount of gold standard data is found to be sufficient to rule out these equilibria., Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991)

Published
2012

12. Bandit Market Makers

Author

Della Penna, Nicolas and Reid, Mark D.

Subjects
Quantitative Finance - Trading and Market Microstructure, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning
Abstract

We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guarantees on the regret of profits while preserving the bounded worst-case losses and computational tractability over combinatorial spaces of the cost function based approach. We present simulation results to better understand the practical behaviour of market makers from the framework., Comment: A previous version of this work appeared in the NIPS 2011 Workshop on Computational Social Science and the Wisdom of the Crowds

Published
2011

14. An Experimental Study of Cryptocurrency Market Dynamics

Author

Program in Media Arts and Sciences (Massachusetts Institute of Technology), Massachusetts Institute of Technology. Media Laboratory, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Krafft, Peter M., Della Penna, Nicolas, Pentland, Alex Paul, Program in Media Arts and Sciences (Massachusetts Institute of Technology), Massachusetts Institute of Technology. Media Laboratory, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Krafft, Peter M., Della Penna, Nicolas, and Pentland, Alex Paul

Abstract

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions. Keywords: Cryptocurrencies; online markets; peer influence; computational social science; online field experiments; digital institutions; design; market design

Published
2020

16. Response

Author

Yao, Jasper Seth, Paguio, Joseph Alexander, Dee, Edward Christopher, Tan, Hanna Clementine, Moulick, Achintya, Milazzo, Carmelo, Jurado, Jerry, Della Penna, Nicolás, and Celi, Leo Anthony

Published
2021
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17. Response to Comment on 'Estimating the reproducibility of psychological science'

Author

Anderson, Christopher J, Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A, Chandler, Jesse, Chartier, Christopher R, Cheung, Felix, Christopherson, Cody D, Cordes, Andreas, Cremata, Edward J, Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A, Frank, Michael C, Grange, James A, Hartshorne, Joshua K, Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J, Lai, Calvin K, Levitan, Carmel A, Miller, Jeremy K, Moore, Katherine S, Meixner, Johannes M, Munafò, Marcus R, Neijenhuijs, Koen I, Nilsonne, Gustav, Nosek, Brian A, Plessow, Franziska, Prenoveau, Jason M, Ricker, Ashley A, Schmidt, Kathleen, Spies, Jeffrey R, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B, van Aert, Robbie C M, van Assen, Marcel A L M, Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, Zuni, Kellylynn, Anderson, Christopher J, Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A, Chandler, Jesse, Chartier, Christopher R, Cheung, Felix, Christopherson, Cody D, Cordes, Andreas, Cremata, Edward J, Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A, Frank, Michael C, Grange, James A, Hartshorne, Joshua K, Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J, Lai, Calvin K, Levitan, Carmel A, Miller, Jeremy K, Moore, Katherine S, Meixner, Johannes M, Munafò, Marcus R, Neijenhuijs, Koen I, Nilsonne, Gustav, Nosek, Brian A, Plessow, Franziska, Prenoveau, Jason M, Ricker, Ashley A, Schmidt, Kathleen, Spies, Jeffrey R, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B, van Aert, Robbie C M, van Assen, Marcel A L M, Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, and Zuni, Kellylynn

Abstract

Gilbert et al. conclude that evidence from the Open Science Collaboration's Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.

Published
2016

18. Response to Comment on 'Estimating the reproducibility of psychological science'

Author

Anderson, Christopher J., Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A., Chandler, Jesse, Chartier, Christopher R., Cheung, Felix, Christopherson, Cody D., Cordes, Andreas, Cremata, Edward J., Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A., Frank, Michael C., Grange, James A., Hartshorne, Joshua K., Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J., Lai, Calvin K., Levitan, Carmel A., Miller, Jeremy K., Moore, Katherine S., Meixner, Johannes M., Munafò, Marcus R., Neijenhuijs, Koen I., Nilsonne, Gustav, Nosek, Brian A., Plessow, Franziska, Prenoveau, Jason M., Ricker, Ashley A., Schmidt, Kathleen, Spies, Jeffrey R., Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B., van Aert, Robbie C. M., van Assen, Marcel A. L. M., Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, Zuni, Kellylynn, Anderson, Christopher J., Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A., Chandler, Jesse, Chartier, Christopher R., Cheung, Felix, Christopherson, Cody D., Cordes, Andreas, Cremata, Edward J., Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A., Frank, Michael C., Grange, James A., Hartshorne, Joshua K., Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J., Lai, Calvin K., Levitan, Carmel A., Miller, Jeremy K., Moore, Katherine S., Meixner, Johannes M., Munafò, Marcus R., Neijenhuijs, Koen I., Nilsonne, Gustav, Nosek, Brian A., Plessow, Franziska, Prenoveau, Jason M., Ricker, Ashley A., Schmidt, Kathleen, Spies, Jeffrey R., Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B., van Aert, Robbie C. M., van Assen, Marcel A. L. M., Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, and Zuni, Kellylynn

Abstract

Gilbert et al. conclude that evidence from the Open Science Collaboration's Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.

Published
2016
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19. Response to Comment on 'Estimating the reproducibility of psychological science'

Author

Social Networks, Solidarity and Inequality, Leerstoel Buskens, Anderson, Christopher J, Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A, Chandler, Jesse, Chartier, Christopher R, Cheung, Felix, Christopherson, Cody D, Cordes, Andreas, Cremata, Edward J, Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A, Frank, Michael C, Grange, James A, Hartshorne, Joshua K, Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J, Lai, Calvin K, Levitan, Carmel A, Miller, Jeremy K, Moore, Katherine S, Meixner, Johannes M, Munafò, Marcus R, Neijenhuijs, Koen I, Nilsonne, Gustav, Nosek, Brian A, Plessow, Franziska, Prenoveau, Jason M, Ricker, Ashley A, Schmidt, Kathleen, Spies, Jeffrey R, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B, van Aert, Robbie C M, van Assen, Marcel A L M, Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, Zuni, Kellylynn, Social Networks, Solidarity and Inequality, Leerstoel Buskens, Anderson, Christopher J, Bahník, Štěpán, Barnett-Cowan, Michael, Bosco, Frank A, Chandler, Jesse, Chartier, Christopher R, Cheung, Felix, Christopherson, Cody D, Cordes, Andreas, Cremata, Edward J, Della Penna, Nicolas, Estel, Vivien, Fedor, Anna, Fitneva, Stanka A, Frank, Michael C, Grange, James A, Hartshorne, Joshua K, Hasselman, Fred, Henninger, Felix, van der Hulst, Marije, Jonas, Kai J, Lai, Calvin K, Levitan, Carmel A, Miller, Jeremy K, Moore, Katherine S, Meixner, Johannes M, Munafò, Marcus R, Neijenhuijs, Koen I, Nilsonne, Gustav, Nosek, Brian A, Plessow, Franziska, Prenoveau, Jason M, Ricker, Ashley A, Schmidt, Kathleen, Spies, Jeffrey R, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B, van Aert, Robbie C M, van Assen, Marcel A L M, Vanpaemel, Wolf, Vianello, Michelangelo, Voracek, Martin, and Zuni, Kellylynn

Published
2016

21. Response to Comment on “Estimating the reproducibility of psychological science”

Author

Anderson, Christopher J., primary, Bahník, Štěpán, additional, Barnett-Cowan, Michael, additional, Bosco, Frank A., additional, Chandler, Jesse, additional, Chartier, Christopher R., additional, Cheung, Felix, additional, Christopherson, Cody D., additional, Cordes, Andreas, additional, Cremata, Edward J., additional, Della Penna, Nicolas, additional, Estel, Vivien, additional, Fedor, Anna, additional, Fitneva, Stanka A., additional, Frank, Michael C., additional, Grange, James A., additional, Hartshorne, Joshua K., additional, Hasselman, Fred, additional, Henninger, Felix, additional, van der Hulst, Marije, additional, Jonas, Kai J., additional, Lai, Calvin K., additional, Levitan, Carmel A., additional, Miller, Jeremy K., additional, Moore, Katherine S., additional, Meixner, Johannes M., additional, Munafò, Marcus R., additional, Neijenhuijs, Koen I., additional, Nilsonne, Gustav, additional, Nosek, Brian A., additional, Plessow, Franziska, additional, Prenoveau, Jason M., additional, Ricker, Ashley A., additional, Schmidt, Kathleen, additional, Spies, Jeffrey R., additional, Stieger, Stefan, additional, Strohminger, Nina, additional, Sullivan, Gavin B., additional, van Aert, Robbie C. M., additional, van Assen, Marcel A. L. M., additional, Vanpaemel, Wolf, additional, Vianello, Michelangelo, additional, Voracek, Martin, additional, and Zuni, Kellylynn, additional

Published
2016
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22. Estimating the reproducibility of psychological science

Author

Aarts, Alexander A., Anderson, Joanna E., Anderson, Christopher J., Attridge, Peter R., Attwood, Angela, Axt, Jordan, Babel, Molly, Bahnik, Stepan, Baranski, Erica, Barnett-Cowan, Michael, Bartmess, Elizabeth, Beer, Jennifer, Bell, Raoul, Bentley, Heather, Beyan, Leah, Binion, Grace, Borsboom, Denny, Bosch, Annick, Bosco, Frank A., Bowman, Sara D., Brandt, Mark J., Braswell, Erin, Brohmer, Hilmar, Brown, Benjamin T., Brown, Kristina, Bruening, Jovita, Calhoun-Sauls, Ann, Callahan, Shannon P., Chagnon, Elizabeth, Chandler, Jesse, Chartier, Christopher R., Cheung, Felix, Christopherson, Cody D., Cillessen, Linda, Clay, Russ, Cleary, Hayley, Cloud, Mark D., Cohn, Michael, Cohoon, Johanna, Columbus, Simon, Cordes, Andreas, Costantini, Giulio, Alvarez, Leslie D. Cramblet, Cremata, Ed, Crusius, Jan, DeCoster, Jamie, DeGaetano, Michelle A., Della Penna, Nicolas, den Bezemer, Bobby, Deserno, Marie K., Devitt, Olivia, Dewitte, Laura, Dobolyi, David G., Dodson, Geneva T., Donnellan, M. Brent, Donohue, Ryan, Dore, Rebecca A., Dorrough, Angela, Dreber, Anna, Dugas, Michelle, Dunn, Elizabeth W., Easey, Kayleigh, Eboigbe, Sylvia, Eggleston, Casey, Embley, Jo, Epskamp, Sacha, Errington, Timothy M., Estel, Vivien, Farach, Frank J., Feather, Jenelle, Fedor, Anna, Fernandez-Castilla, Belen, Fiedler, Susann, Field, James G., Fitneva, Stanka A., Flagan, Taru, Forest, Amanda L., Forsell, Eskil, Foster, Joshua D., Frank, Michael C., Frazier, Rebecca S., Fuchs, Heather, Gable, Philip, Galak, Jeff, Galliani, Elisa Maria, Gampa, Anup, Garcia, Sara, Gazarian, Douglas, Gilbert, Elizabeth, Giner-Sorolla, Roger, Gloeckner, Andreas, Goellner, Lars, Goh, Jin X., Goldberg, Rebecca, Goodbourn, Patrick T., Gordon-McKeon, Shauna, Gorges, Bryan, Gorges, Jessie, Goss, Justin, Graham, Jesse, Grange, James A., Gray, Jeremy, Hartgerink, Chris, Hartshorne, Joshua, Hasselman, Fred, Hayes, Timothy, Heikensten, Emma, Henninger, Felix, Hodsoll, John, Holubar, Taylor, Hoogendoorn, Gea, Humphries, Denise J., Hung, Cathy O. -Y., Immelman, Nathali, Irsik, Vanessa C., Jahn, Georg, Jaekel, Frank, Jekel, Marc, Johannesson, Magnus, Johnson, Larissa G., Johnson, David J., Johnson, Kate M., Johnston, William J., Jonas, Kai, Joy-Gaba, Jennifer, Kappes, Heather Barry, Kelso, Kim, Kidwell, Mallory C., Kim, Seung Kyung, Kirkhart, Matthew, Kleinberg, Bennett, Knežević, Goran, Kolorz, Franziska Maria, Kossakowski, Jolanda J., Krause, Robert Wilhelm, Krijnen, Job, Kuhlmann, Tim, Kunkels, Yoram K., Kyc, Megan M., Lai, Calvin K., Laique, Aamir, Lakens, Daniel, Lane, Kristin A., Lassetter, Bethany, Lazarević, Ljiljana, LeBel, Etienne P., Lee, Key Jung, Lee, Minha, Lemm, Kristi, Levitan, Carmel A., Lewis, Melissa, Lin, Lin, Lin, Stephanie, Lippold, Matthias, Loureiro, Darren, Luteijn, Ilse, Mackinnon, Sean, Mainard, Heather N., Marigold, Denise C., Martin, Daniel P., Martinez, Tylar, Masicampo, E. J., Matacotta, Josh, Mathur, Maya, May, Michael, Mechin, Nicole, Mehta, Pranjal, Meixner, Johannes, Melinger, Alissa, Miller, Jeremy K., Miller, Mallorie, Moore, Katherine, Moeschl, Marcus, Motyl, Matt, Mueller, Stephanie M., Munafo, Marcus, Neijenhuijs, Koen I., Nervi, Taylor, Gandalf, Nicolas, Nilsonne, Gustav, Nosek, Brian A., Nuijten, Michele B., Olsson, Catherine, Osborne, Colleen, Ostkamp, Lutz, Pavel, Misha, Penton-Voak, Ian S., Perna, Olivia, Pernet, Cyril, Perugini, Marco, Pipitone, R. Nathan, Pitts, Michael, Plessow, Franziska, Prenoveau, Jason M., Rahal, Rima-Maria, Ratliff, Kate A., Reinhard, David, Renkewitz, Frank, Ricker, Ashley A., Rigney, Anastasia, Rivers, Andrew M., Roebke, Mark, Rutchick, Abraham M., Ryan, Robert S., Sahin, Onur, Saide, Anondah, Sandstrom, Gillian M., Santos, David, Saxe, Rebecca, Schlegelmilch, Rene, Schmidt, Kathleen, Scholz, Sabine, Seibel, Larissa, Selterman, Dylan Faulkner, Shaki, Samuel, Simpson, William B., Sinclair, H. Colleen, Skorinko, Jeanine L. M., Slowik, Agnieszka, Snyder, Joel S., Soderberg, Courtney, Sonnleitner, Carina, Spencer, Nick, Spies, Jeffrey R., Steegen, Sara, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B., Talhelm, Thomas, Tapia, Megan, te Dorsthorst, Anniek, Thomae, Manuela, Thomas, Sarah L., Tio, Pia, Traets, Frits, Tsang, Steve, Tuerlinckx, Francis, Turchan, Paul, Valasek, Milan, van 't Veer, Anna E., Van Aert, Robbie, van Assen, Marcel, van Bork, Riet, van de Ven, Mathijs, van den Bergh, Don, van der Hulst, Marije, van Dooren, Roel, van Doorn, Johnny, van Renswoude, Daan R., van Rijn, Hedderik, Vanpaemel, Wolf, Echeverria, Alejandro Vasquez, Vazquez, Melissa, Velez, Natalia, Vermue, Marieke, Verschoor, Mark, Vianello, Michelangelo, Voracek, Martin, Vuu, Gina, Wagenmakers, Eric-Jan, Weerdmeester, Joanneke, Welsh, Ashlee, Westgate, Erin C., Wissink, Joeri, Wood, Michael, Woods, Andy, Wright, Emily, Wu, Sining, Zeelenberg, Marcel, Zuni, Kellylynn, Aarts, Alexander A., Anderson, Joanna E., Anderson, Christopher J., Attridge, Peter R., Attwood, Angela, Axt, Jordan, Babel, Molly, Bahnik, Stepan, Baranski, Erica, Barnett-Cowan, Michael, Bartmess, Elizabeth, Beer, Jennifer, Bell, Raoul, Bentley, Heather, Beyan, Leah, Binion, Grace, Borsboom, Denny, Bosch, Annick, Bosco, Frank A., Bowman, Sara D., Brandt, Mark J., Braswell, Erin, Brohmer, Hilmar, Brown, Benjamin T., Brown, Kristina, Bruening, Jovita, Calhoun-Sauls, Ann, Callahan, Shannon P., Chagnon, Elizabeth, Chandler, Jesse, Chartier, Christopher R., Cheung, Felix, Christopherson, Cody D., Cillessen, Linda, Clay, Russ, Cleary, Hayley, Cloud, Mark D., Cohn, Michael, Cohoon, Johanna, Columbus, Simon, Cordes, Andreas, Costantini, Giulio, Alvarez, Leslie D. Cramblet, Cremata, Ed, Crusius, Jan, DeCoster, Jamie, DeGaetano, Michelle A., Della Penna, Nicolas, den Bezemer, Bobby, Deserno, Marie K., Devitt, Olivia, Dewitte, Laura, Dobolyi, David G., Dodson, Geneva T., Donnellan, M. Brent, Donohue, Ryan, Dore, Rebecca A., Dorrough, Angela, Dreber, Anna, Dugas, Michelle, Dunn, Elizabeth W., Easey, Kayleigh, Eboigbe, Sylvia, Eggleston, Casey, Embley, Jo, Epskamp, Sacha, Errington, Timothy M., Estel, Vivien, Farach, Frank J., Feather, Jenelle, Fedor, Anna, Fernandez-Castilla, Belen, Fiedler, Susann, Field, James G., Fitneva, Stanka A., Flagan, Taru, Forest, Amanda L., Forsell, Eskil, Foster, Joshua D., Frank, Michael C., Frazier, Rebecca S., Fuchs, Heather, Gable, Philip, Galak, Jeff, Galliani, Elisa Maria, Gampa, Anup, Garcia, Sara, Gazarian, Douglas, Gilbert, Elizabeth, Giner-Sorolla, Roger, Gloeckner, Andreas, Goellner, Lars, Goh, Jin X., Goldberg, Rebecca, Goodbourn, Patrick T., Gordon-McKeon, Shauna, Gorges, Bryan, Gorges, Jessie, Goss, Justin, Graham, Jesse, Grange, James A., Gray, Jeremy, Hartgerink, Chris, Hartshorne, Joshua, Hasselman, Fred, Hayes, Timothy, Heikensten, Emma, Henninger, Felix, Hodsoll, John, Holubar, Taylor, Hoogendoorn, Gea, Humphries, Denise J., Hung, Cathy O. -Y., Immelman, Nathali, Irsik, Vanessa C., Jahn, Georg, Jaekel, Frank, Jekel, Marc, Johannesson, Magnus, Johnson, Larissa G., Johnson, David J., Johnson, Kate M., Johnston, William J., Jonas, Kai, Joy-Gaba, Jennifer, Kappes, Heather Barry, Kelso, Kim, Kidwell, Mallory C., Kim, Seung Kyung, Kirkhart, Matthew, Kleinberg, Bennett, Knežević, Goran, Kolorz, Franziska Maria, Kossakowski, Jolanda J., Krause, Robert Wilhelm, Krijnen, Job, Kuhlmann, Tim, Kunkels, Yoram K., Kyc, Megan M., Lai, Calvin K., Laique, Aamir, Lakens, Daniel, Lane, Kristin A., Lassetter, Bethany, Lazarević, Ljiljana, LeBel, Etienne P., Lee, Key Jung, Lee, Minha, Lemm, Kristi, Levitan, Carmel A., Lewis, Melissa, Lin, Lin, Lin, Stephanie, Lippold, Matthias, Loureiro, Darren, Luteijn, Ilse, Mackinnon, Sean, Mainard, Heather N., Marigold, Denise C., Martin, Daniel P., Martinez, Tylar, Masicampo, E. J., Matacotta, Josh, Mathur, Maya, May, Michael, Mechin, Nicole, Mehta, Pranjal, Meixner, Johannes, Melinger, Alissa, Miller, Jeremy K., Miller, Mallorie, Moore, Katherine, Moeschl, Marcus, Motyl, Matt, Mueller, Stephanie M., Munafo, Marcus, Neijenhuijs, Koen I., Nervi, Taylor, Gandalf, Nicolas, Nilsonne, Gustav, Nosek, Brian A., Nuijten, Michele B., Olsson, Catherine, Osborne, Colleen, Ostkamp, Lutz, Pavel, Misha, Penton-Voak, Ian S., Perna, Olivia, Pernet, Cyril, Perugini, Marco, Pipitone, R. Nathan, Pitts, Michael, Plessow, Franziska, Prenoveau, Jason M., Rahal, Rima-Maria, Ratliff, Kate A., Reinhard, David, Renkewitz, Frank, Ricker, Ashley A., Rigney, Anastasia, Rivers, Andrew M., Roebke, Mark, Rutchick, Abraham M., Ryan, Robert S., Sahin, Onur, Saide, Anondah, Sandstrom, Gillian M., Santos, David, Saxe, Rebecca, Schlegelmilch, Rene, Schmidt, Kathleen, Scholz, Sabine, Seibel, Larissa, Selterman, Dylan Faulkner, Shaki, Samuel, Simpson, William B., Sinclair, H. Colleen, Skorinko, Jeanine L. M., Slowik, Agnieszka, Snyder, Joel S., Soderberg, Courtney, Sonnleitner, Carina, Spencer, Nick, Spies, Jeffrey R., Steegen, Sara, Stieger, Stefan, Strohminger, Nina, Sullivan, Gavin B., Talhelm, Thomas, Tapia, Megan, te Dorsthorst, Anniek, Thomae, Manuela, Thomas, Sarah L., Tio, Pia, Traets, Frits, Tsang, Steve, Tuerlinckx, Francis, Turchan, Paul, Valasek, Milan, van 't Veer, Anna E., Van Aert, Robbie, van Assen, Marcel, van Bork, Riet, van de Ven, Mathijs, van den Bergh, Don, van der Hulst, Marije, van Dooren, Roel, van Doorn, Johnny, van Renswoude, Daan R., van Rijn, Hedderik, Vanpaemel, Wolf, Echeverria, Alejandro Vasquez, Vazquez, Melissa, Velez, Natalia, Vermue, Marieke, Verschoor, Mark, Vianello, Michelangelo, Voracek, Martin, Vuu, Gina, Wagenmakers, Eric-Jan, Weerdmeester, Joanneke, Welsh, Ashlee, Westgate, Erin C., Wissink, Joeri, Wood, Michael, Woods, Andy, Wright, Emily, Wu, Sining, Zeelenberg, Marcel, and Zuni, Kellylynn

Abstract

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

Published
2015

23. Constructing Consumer Sentiment Index for U.S. Using Google Searches

Author

Della Penna, Nicolas and Huang, Haifang

Subjects
jel:E27, jel:C53, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, jel:E21, consumer sentiment, consumer confidence, leading economic indicators
Abstract

We construct a consumer sentiment index for the U.S. using the popularity trends of selected Google searches. The final index consists of four components and is highly correlated with the Index of Consumer Sentiment from the University of Michigan and the Consumer Confidence Index from the Conference Board. Among the three sentiment indices, the Google search-based index (SBI) leads in time and predicts other indices. In terms of forecasting consumer spending, the SBI outperforms both the ICS and the CCI and provides independent information. For robustness, we use multiple measures of consumer spending and a range of statistical specifications. The finding is robust.

Published
2009

25. New Objective Functions for Social Collaborative Filtering

Author

Noel, Joseph, Sanner, Scott, Tran, Khoi-Nguyen, Christen, Peter, Xie, Lexing, Bonilla, Edwin, Abbasnejad, Ehsan, Della Penna, Nicolas, Noel, Joseph, Sanner, Scott, Tran, Khoi-Nguyen, Christen, Peter, Xie, Lexing, Bonilla, Edwin, Abbasnejad, Ehsan, and Della Penna, Nicolas

Abstract

This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks poses the more complex problem of learning user preferences from a rich and complex set of user profile and interaction information. Many existing social CF methods have extended traditional CF matrix factorization, but have overlooked important aspects germane to the social setting. We propose a unified framework for social CF matrix factorization by introducing novel objective functions for training. Our new objective functions have three key features that address main drawbacks of existing approaches: (a) we fully exploit feature-based user similarity, (b) we permit direct learning of user-to-user information diffusion, and (c) we leverage co-preference (dis)agreement between two users to learn restricted areas of common interest. We evaluate these new social CF objectives, comparing them to each other and to a variety of (social) CF baselines, and analyze user behavior on live user trials in a customdeveloped Facebook App involving data collected over five months from over 100 App users and their 37,000+ friends.

Published
2012

26. Interpreting Prediction Markets: A Stochastic Approach

Author

Bartlett, P., Frongillo, Rafael M, Della Penna, Nicolas, Reid, Mark, Bartlett, P., Frongillo, Rafael M, Della Penna, Nicolas, and Reid, Mark

Abstract

We strengthen recent connections between prediction markets and learning by showing that a natural class of market makers can be understood as performing stochastic mirror descent when trader demands are sequentially drawn from a fixed distribution. This

Published
2012

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