Tom Everitt
Impact in
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- Ethics and Social Impacts of AI
Papers in
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- Bayesian Modeling and Causal Inference 3
- Logic, Reasoning, and Knowledge 2
- Evolutionary Algorithms and Applications 1
- Machine Learning and Algorithms 1
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- Computability, Logic, AI Algorithms 3
- Co-authors
- Ryan M. Carey (5 shared papers)Sebastian Farquhar (2 shared papers)Ben Goertzel (1 shared paper)Alexey Potapov (1 shared paper)Zachary Kenton (1 shared paper)Jonathan G. Richens (1 shared paper)Ramana Kumar (1 shared paper)Marcus Hütter (2 shared papers)
- Journals
- Artificial Intelligence (2 papers)Theory and Decision (1 paper)Lecture notes in computer science (1 paper)KTH Publication Database DiVA (KTH Royal Institute of Technology) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (3 papers)
- Partner nations
- United KingdomAustraliaSweden
In The Last Decade
Tom Everitt
7 papers receiving 31 citations
Peers
Comparison fields: 5 of 34
- Safety Research 11
- Health Informatics 1
- Artificial Intelligence 12
- Safety, Risk, Reliability and Quality 3
- Management Information Systems 2
Countries citing papers authored by Tom Everitt
This map shows the geographic impact of Tom Everitt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tom Everitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Everitt more than expected).
Fields of papers citing papers by Tom Everitt
This network shows the impact of papers produced by Tom Everitt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tom Everitt. The network helps show where Tom Everitt may publish in the future.
Co-authors
The 13 scholars most cited alongside Tom Everitt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 9 | |
| 2 | 2017 | 8 | |
| 3 | 2022 | 4 | |
| 4 | 2022 | 3 | |
| 5 | 2014 | 3 | |
| 6 | 2021 | 3 | |
| 7 | 2023 | 2 | |
| 8 | Universal Induction and Optimisation: No Free Lunch | 2013 | 1 |
| 9 | 2021 | 0 | |
| 10 | 2022 | 0 |
About Tom Everitt
Tom Everitt is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Safety Research, Management Science and Operations Research and Numerical Analysis, having authored 10 papers that have together received 33 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Computability, Logic, AI Algorithms (3 papers), Logic, Reasoning, and Knowledge (2 papers), Ethics and Social Impacts of AI (2 papers), Evolutionary Algorithms and Applications (1 paper), Experimental Behavioral Economics Studies (1 paper), Advanced Optimization Algorithms Research (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Safety Research (11 citations), Health Informatics (1 citation), Artificial Intelligence (12 citations), Safety, Risk, Reliability and Quality (3 citations) and Management Information Systems (2 citations). Tom Everitt has collaborated with scholars based in United Kingdom, Australia and Sweden. Frequent co-authors include Ryan M. Carey, Sebastian Farquhar, Ben Goertzel, Alexey Potapov, Zachary Kenton, Jonathan G. Richens, Ramana Kumar, Marcus Hütter, Michael Wooldridge and Silvia Chiappa. Their work appears in journals such as Artificial Intelligence, Theory and Decision, Lecture notes in computer science, KTH Publication Database DiVA (KTH Royal Institute of Technology) and Proceedings of the AAAI Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.