Nicholas A. Christakis ✓
@NAChristakis · Vermont, USA · joined 29 Aug 2012
Sterling Professor of Social and Natural Science at Yale. Physician. Author of Apollo's Arrow; Blueprint; and Connected. ::: More active at azure skies.
185 824Followers
750Following
53 694Posts total
59.4KViews on collected posts
Latest posts
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Fine essay via @paulg "How Universities Should Prepare Founders": https://t.co/EMKqB3Nimt
Would that universities would listen, as there would be other benefits to pedagogy, too.
"The people you need to impress are users, not investors, and the way you impress them is with
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@NAChristakis Super cool thread, Dr. Christakis. I was at the ASA conference earlier this month & one of your co-authors presented your joint work on social networks and homophily. So happy to see cognitive social psychology and network science complementing each other.
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Experiments and field trials show how to exploit an understanding of social network structure and function to enhance human welfare. We can use an understanding of social networks, and of social contagion theory, to intervene in human populations, whether online or in person. 20/
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Humans are naturally embedded in complex, face-to-face social networks. These networks obey particular mathematical and social rules, and taking this into account offers profound new opportunities to understand and modify human behavior and collective outcomes , including public
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Social contagion is relevant to phenomena as diverse as public goods production, inequality, technology adoption, economic productivity, political mobilization, misinformation, violence, emotional experience, health behavior, and epidemic disease. 19/
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“Bringing Leaders of Network Subgroups Closer Together Does Not Facilitate Consensus,” Scientific Reports2024;14:30183. We find no evidence that the geodesic distance between leaders of two groups is a significant factor in the probability of reaching consensus.
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“Simple Autonomous Agents Can Enhance Creative Semantic Discovery in Human Groups,” Nature Communications2024;14:5212. Groups are better able to identify and preserve innovations than individuals. We show that bots can enhance the creativity of human groups.
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“Emergence and Collapse of Reciprocity in Semiautomatic Driving Coordination Experiments with Humans,” PNAS2023;120:e2307804120. Using remote-control cars in a game of “chicken,” we show that altruism collapses when people leave coordination decisions to machines. https://t.co/s1
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“Status Invisibility Alleviates the Economic Gradient in Happiness in Social Network Experiments,” Nature Mental Health 2023;1:990. Making wealth invisible improves the subjective well-being of poor subjects and thus alleviates the economic gradient in SWB.
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“Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups,” iScience 2020;23:101438. Simple AI bots can foster cooperation in human groups by using a re-wiring strategy designed to support the development of cooperative clusters. https://t.co/aPVE4ibG4H
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“Assortative Mixing and Resource Inequality Enhance Collective Welfare in Sharing Networks,” PNAS 2019;116:22442. Resources and ties can be allocated in a network in a way that optimizes collective welfare, but at no increased overall cost. https://t.co/S6uxOJbWrt 11/
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“Resource Sharing in Technologically Defined Social Networks,” Nature Communications 2019;10:1079. Sharing-economy networks are changing the way humans trade and collaborate. These networks can be structured to maximize collective welfare. https://t.co/I98F4ofnCW 10/
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“Intermediate Levels of Network Fluidity Amplify Economic Growth and Mitigate Economic Inequality in Experimental Social Networks,” Sociological Science 2015;2:544. Increasing network fluidity fosters economic growth and lowers inequality, up to a point. https://t.co/t9oyyIdr6x
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“Locally Noisy Autonomous Agents Improve Global Human Coordination in Network Experiments,” Nature2017;545:370. AI bots in groups facing a coordination challenge demonstrate the benefits of noise in collective decision making. Bots help humans to help themselves.
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“Static Network Structure Can Stabilize Human Cooperation,” PNAS 2014;111:17093. Consistent with evolutionary game theory, when the benefit-to-cost ratio of cooperation exceeds the average network degree (b/c>k), even a static network structure can stabilize cooperation.
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“Inequality and Visibility of Wealth in Experimental Social Networks,” Nature 2015;526:426. Making wealth visible in groups lowers overall cooperation, connection, and wealth. High initial levels of economic inequality alone, however, have few bad welfare effects.
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“Quality Versus Quantity of Social Ties in Experimental Cooperative Networks,” Nature Communications 2013;4:2814. There is a Goldilocks effect of network dynamism on cooperation. Optimal levels of cooperation are achieved at intermediate levels of change in social ties.
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“Dynamic Social Networks Promote Cooperation in Experiments with Humans,” PNAS 2011;108:19193. Experiments confirm evolutionary game theoretic models, demonstrating important role that dynamic social networks play in supporting large-scale cooperation. https://t.co/UQGONKmowi 4/
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“Cooperative Behavior Cascades in Human Social Networks,” PNAS 2010;107:5334. These results show experimentally that cooperative behavior cascades in human social networks, to three degrees of separation, via social contagion. https://t.co/lhd8qM9mwn 3/ https://t.co/tQms1YeeP5
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To sustain cooperation and related collective action in human groups, one way is “adding structure” by arranging people in networks or otherwise constraining their interactions (classic review: https://t.co/ZheP9X0f28).
We have used experiments to explore this. 2/
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What role do social networks – and the precise nature and structure of social interactions – play in the production of public goods and in human welfare? What about social contagion? A thread on #HNL experiments with many thousands of participants from 2010 to the present. 1/ htt
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New FLOS video: How to Assemble your Dissertation Committee: https://t.co/dWD1V2YEvI
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@PNASNews On the path to machine olfaction. https://t.co/UZN8ilSQGH
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@NAChristakis @PNASNews Does this connect with networks via Nietszhe's insistence that people connect by smell?
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Olfaction lacks a quantitative framework similar to vision and hearing.
In new #HNL work in @PNASNews, we show that odor mixture perception can be predicted. This lays a foundation for digital olfaction, machine-readable smell metrics, next-generation electronic noses, and
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Sometimes, when you are doing research, you become so obsessed with hidden reasons for things that you miss the manifest reasons.
https://t.co/wQuAxIc2SX
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Against accounts of the same size
26 posts from the last 90 days, next to the 100K–1M follower range. reaches fewer people than peers of the same size.
Median views1 079this account5 886median for 100K–1M
Reach, %0.58%this account1.87%median for 100K–1M
Engagement, %1.16%this account1.16%median for 100K–1M
| Metric | This account | Median for 100K–1M | Ratio |
|---|---|---|---|
| Median views per post | 1 079 | 5 886 | 0.18× |
| Reach (views ÷ followers) | 0.58% | 1.87% | 0.31× |
| Engagement rate | 1.16% | 1.16% | 0.99× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
19326 Aug
204
199
212
2.5K
219
2.2K
181
182
1.5K
183
1.5K
2827 Aug
6.2K28 Aug
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
2.07%26 Aug
2.45%
2.01%
2.83%
0.44%
3.20%
0.49%
3.87%
3.30%
0.47%
2.73%
0.59%
3.57%27 Aug
0.21%28 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.16%.
What the audience does
Likes55.8%282 in total
Reposts12.1%61 in total
Replies5.1%26 in total
Quotes0.2%1 in total
Bookmarks26.7%135 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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