According to a survey conducted in April 2024 in the United States, one in five online dating service users had lied about their age on their dating profile, while 14 percent had lied about their income. A further 14 percent had lied about their hobbies and interests, and 12 percent had lied about their height.

I experimentally investigate the hypothesis that many people avoid lying even in a situation where doing so would result in a Pareto improvement. Replicating (Erat and Gneezy, Management Science 58, 723-733, 2012), I find that a significant fraction of subjects tell the truth in a sender-receiver game where both subjects earn a higher payoff when the partner makes an incorrect guess regarding the roll of a die. However, a non-incentivized questionnaire indicates that the vast majority of these subjects expected their partner not to follow their message. I conduct two new experiments explicitly designed to test for a 'pure' aversion to lying, and find no evidence for the existence of such a motivation. I discuss the implications of the findings for moral behavior and rule following more generally.

This statistic shows the results of a survey among adult Americans in 2016 on situations in which lying is okay. During the survey, 18 percent of respondents stated that lying in order to avoid hurting someone's feelings is often okay, while 58 percent said it was sometimes okay, and 24 percent thought it was never okay to lie in order to avoid hurting someone's feelings.

This statistic illustrates the results of a survey regarding the public opinion on the amount of lying and misuse of facts in politics and media compared to 30 years ago in selected countries in Europe in 2018. According to data published by IPSOS, ** percent of Turkish respondents thought that the amount of lying and misuse of facts in politics and the media had increased compared to 30 years ago.

This paper studies lying. An agent randomly picks a number from a known distribution. She can then report any number and receive a monetary payoff based only on her report. The paper presents a model of lying costs that generates hypotheses regarding behavior. In an experiment, we find that the highest fraction of lies is from reporting the maximal outcome, but some participants do not make the maximal lie. More participants lie partially when the experimenter cannot observe their outcomes than when the experimenter can verify the observed outcome. Partial lying increases when the prior probability of the highest outcome decreases.

This statistic shows the results of a survey among adult Americans in 2016 on how often they feel the need to lie or cheat. During the survey, 13 percent of respondents stated they occasionally have to lie or to cheat.

According to a survey conducted in the United Kingdom (UK) in 2023, ** percent of online dating service users had lied about their age, while one in ten had lied about their name. Overall, ***** percent had lied about their job, and **** percent had lied about their current relationship status.

This code and dataset replicate the results in Paul Clist and Ying-yi Hong (2019) Why Do We Lie? Distinguishing Between Competing Lying Theories CBESS working paper 19-03, available at: https://ueaeco.github.io/working-papers/papers/cbess/UEA-CBESS-19-03.pdf The second experiment was preregistered: https://www.socialscienceregistry.org/trials/3547

To address the lack of a Chinese context based lie detection dataset in current research, we have developed SEUMLD, which is the first publicly available multimodal lie detection dataset based on Chinese conversations. SEUMLD contains data in three modalities: video, audio, and electrocardiogram signals. In order to effectively stimulate the participants' motivation to lie, we designed a paradigm of simulated crime and simulated interrogation experiments. By recording multimodal signals of participants during simulated interrogation, SEUMLD collected data from 76 participants who had lived in a Chinese language environment for a long time, totaling 3224 conversations. This dataset provides coarse-grained annotation for identifying whether participants lie throughout the entire conversation, as well as fine-grained annotation for precise segmentation of each conversation.

Note. Internal consistency (α), mean (), and standard deviation (s) statistics are presented for each of the four lying subscales, for P1 and P2 participant samples.Descriptive Statistics for Lying Subscales.

Mistakes and overconfidence in detecting lies could help lies spread. Participants in our experiments observe videos in which senders either tell the truth or lie, and are incentivized to distinguish between them. We find that participants fail to detect lies, but are overconfident about their ability to do so. We use these findings to study the determinants of sharing and its effect on lie detection, finding that even when incentivized to share truthful videos, participants are more likely to share lies. Moreover, the receivers are more likely to believe shared videos. Combined, the tendency to believe lies increases with sharing.

The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.

We run an experimental study using sender-receiver games to evaluate how senders' willingness to lie to others compares to their willingness to tell hard truths, i.e., promote an outcome that the sender know is unfair to the receiver without explicitly lying. Unlike in previous work on lying when it has consequences, we do not find that antisocial behavior is less frequent when it involves lying than when it does not. In fact, we find the opposite result in the setting where there is social contact between senders and receivers, and receivers have enough information to judge whether they have been treated unfairly. In this setting, we find that senders prefer to hide behind a lie and implement the antisocial outcome by being dishonest rather than by telling the truth. These results are consistent with social image costs depending on the social proximity between senders and receivers, especially when receivers can judge the kindness of the senders' actions.

Merged data set of all raw data of the meta study

What are the top vacation rentals in Lies? How many vacation rentals have private pools in Lies? Which vacation homes in Lies are best for families? How many Rentbyowner vacation rentals are available in Lies?

People tend to be bad at explicitly detecting lies. However, indirect veracity judgments and physiological responses may yield above-chance levels of accuracy in differentiating lies from the truth. If lies induce a threat response, vasoconstriction should reduce peripheral cutaneous blood flow, leading to finger temperature drops when confronted with a lie compared to the truth. Participants (N = 95) observed people telling lies or the truth about their social relationships, during which participants’ fingertip temperature was recorded via infrared thermal imaging. Results suggested that the accuracy of explicit veracity categorizations remained at chance levels. Judgments of story-tellers’ likability and trustworthiness as indirect veracity measures, as well as observers’ fingertip temperatures as a physiological veracity measure significantly differed between lies and true stories. However, the effects pointed in the opposite direction of our expectations: participants liked liars better than truth-tellers and trusted liars more; and fingertip temperatures increased while confronted with lies compared to true stories. We discuss that studying observers’ physiological responses may be a useful window to lie detection, but requires future investigation.

Financial overview and grant giving statistics of Friends of Lied

The null hypothesis in the binomial test is the case in which two categories are equally likely to occur. When this test is statistically significant one category is much likely to occur than the other. Our data show that the truth responses are significantly more likely to occur in all conditions except in Unfavourable Reality i.e. when OPs won and Ss lost. In this case, lie and truth responses were comparable both in the No-Presence Group (p = .12) and in the Presence Group (p = .12).

Summary of currently used data sets on lie detection.

Data and replication code for ``Lying for Trump? Elite Cue-Taking and Expressive Responding on Vote Method".