Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players

March, Christoph

doi:10.1016/j.joep.2021.102426

Cited by 28 publications

(16 citation statements)

References 148 publications

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“…AI contributes directly to these evolving decision support systems targeted to improve efficiency and improving worst-case outcomes (e.g. the case of human–machine decision problematic, see Yigitcanlar et al 2020c ; March 2021 ).…”

Section: Literature Backgroundmentioning

confidence: 99%

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

2022

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Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness.

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Section: Literature Backgroundmentioning

confidence: 99%

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

2022

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“…(2) Strong logical processing ability When there are safety problems in the system, if there are very complex virexploits or mesh attacks, it is necessary to have a set of safe and reliable technologies [12][13]. AI skill can simulate complex virexploits or meshs through its superior logical processing power, which is beyond the reach of the human mind.…”

Section: Ai Skillmentioning

confidence: 99%

Application of Artificial Intelligence (AI) Technology in Accounting Machine Network (CN) Safety

2022

IJFET

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AI skill is a very crucial skill, this skill has a very large value for human exploit. At present, AI skill has been utilized reliably in many professional domains, such as CN safety. In daily office work, folk can exploit this skill to meet the daily safety detection of information transmission and data operation. The concept of this skill is well known, and many books have been written about the key technologies. This article studies the adhibition of AI skill in CN safety analysis and processing, explains the specific performance of AI skill in CN safety. Using ANN algorithm, this paper constructs a security model of artificial intelligence technology in computer network, and carries out the simulation experiment. The data show that AI skill has high performance in CN safety.

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“…The example models were discussed by Levy et al 26 and March. 27 Levy et al 26 have applied ABM and game theory to study the cooperation and dynamic changes of road networks. The work suggests employing human subject experiments to verify the model parameters as future work.…”

Section: Introductionmentioning

confidence: 99%

“…The work suggests employing human subject experiments to verify the model parameters as future work. March 27 has surveyed 162 experimental studies to present the state-of-the-art about strategic interactions between human players and artificial agents. March 27 suggests that the outcomes of strategic interaction change when humans interact with computer agents rather than other humans.…”

Section: Introductionmentioning

confidence: 99%

Computerized agents versus human agents in finding core coalition in glove games

2022

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One of the challenges for agent-based modeling is being able to incorporate human behavior. Human behavior is a multifaceted phenomenon, with strategic coalition formation being one form. A hybrid agent-based modeling approach, called ABMSCORE, has been derived to emulate strategic group formation. In this paper, we describe a simulation experiment to compare the ABMSCORE with actual human behavior. The comparison criterion is the respective rates of finding an ideal coalition. In our experimental design, we go to great lengths to ensure the similarity of the scenarios in the two trial types: trials with computerized agents only and trials involving human participants when one of the computerized agents is replaced by an actual human. We did this to limit the number of possible extraneous variables introduced into the experimental system. The scenario considered is the glove game, a standard cooperative game that has been previously used in human experiments. Our results indicate that the ABMSCORE model produces similar rates of finding the ideal coalition as the human players; however, there are some limitations. This research provides evidence for using the ABMSCORE modeling approach to model human strategic coalition formation in agent-based models.

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Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players

Cited by 28 publications

References 148 publications

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

Application of Artificial Intelligence (AI) Technology in Accounting Machine Network (CN) Safety

Computerized agents versus human agents in finding core coalition in glove games

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