Metzler, H., Rimé, B., Pellert, M., Niederkrotenthaler, T., Di Natale, A., & Garcia, D. (2022). Collective emotions during the COVID-19 outbreak. Emotion. https://doi.org/10.1037/emo0001111

@article{metzlerCollectiveEmotionsCOVID192022, title = {Collective Emotions during the {{COVID-19}} Outbreak.}, author = {Metzler, Hannah and Rim{'e}, Bernard and Pellert, Max and Niederkrotenthaler, Thomas and Di Natale, Anna and Garcia, David}, year = {2022}, month = jul, journal = {Emotion}, issn = {1931-1516, 1528-3542}, doi = {10.1037/emo0001111}, abstract = {The COVID-19 pandemic has exposed the world’s population to unprecedented health threats and changes to social life. High uncertainty about the novel disease and its social and economic consequences, together with increasingly stringent governmental measures against the spread of the virus, likely elicited strong emotional responses. We analyzed the digital traces of emotional expressions in tweets during 5 weeks after the start of outbreaks in 18 countries and six different languages. We observed an early strong upsurge of anxiety-related terms in all countries, which was related to the growth in cases and increases in the stringency of governmental measures. Anxiety expression gradually relaxed once stringent measures were in place, possibly indicating that people were reassured. Sadness terms rose and anger terms decreased with or after an increase in the stringency of measures and remained stable as long as measures were in place. Positive emotion words only decreased slightly and briefly in a few countries. Our results reveal some of the most enduring changes in emotional expression observed in long periods of social media data. Such sustained emotional expression could indicate that interactions between users led to the emergence of collective emotions. Words that frequently occurred in tweets suggest a shift in topics of conversation across all emotions, from political ones in 2019, to pandemic related issues during the outbreak, including everyday life changes, other people, and health. This kind of time-sensitive analyses of large-scale samples of emotional expression have the potential to inform risk communication.}, langid = {english} }
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The COVID-19 pandemic has exposed the world’s population to unprecedented health threats and changes to social life. High uncertainty about the novel disease and its social and economic consequences, together with increasingly stringent governmental measures against the spread of the virus, likely elicited strong emotional responses. We analyzed the digital traces of emotional expressions in tweets during 5 weeks after the start of outbreaks in 18 countries and six different languages. We observed an early strong upsurge of anxiety-related terms in all countries, which was related to the growth in cases and increases in the stringency of governmental measures. Anxiety expression gradually relaxed once stringent measures were in place, possibly indicating that people were reassured. Sadness terms rose and anger terms decreased with or after an increase in the stringency of measures and remained stable as long as measures were in place. Positive emotion words only decreased slightly and briefly in a few countries. Our results reveal some of the most enduring changes in emotional expression observed in long periods of social media data. Such sustained emotional expression could indicate that interactions between users led to the emergence of collective emotions. Words that frequently occurred in tweets suggest a shift in topics of conversation across all emotions, from political ones in 2019, to pandemic related issues during the outbreak, including everyday life changes, other people, and health. This kind of time-sensitive analyses of large-scale samples of emotional expression have the potential to inform risk communication.

Pellert, M., Metzler, H., Matzenberger, M., & Garcia, D. (2022). Validating daily social media macroscopes of emotions. Scientific Reports, 12(1), 11236. https://doi.org/10.1038/s41598-022-14579-y

@article{pellertValidatingDailySocial2022, title = {Validating Daily Social Media Macroscopes of Emotions}, author = {Pellert, Max and Metzler, Hannah and Matzenberger, Michael and Garcia, David}, year = {2022}, month = dec, journal = {Scientific Reports}, volume = {12}, number = {1}, pages = {11236}, issn = {2045-2322}, doi = {10.1038/s41598-022-14579-y}, abstract = {Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.}, langid = {english} }
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Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.

Metzler, H., Pellert, M., & Garcia, D. (2022). Using Social Media Data to Capture Emotions Before and During COVID-19 (World Happiness Report 2022, p. 30).

@report{metzlerUsingSocialMedia2022, title = {Using {{Social Media Data}} to {{Capture Emotions Before}} and {{During COVID-19}}}, author = {Metzler, Hannah and Pellert, Max and Garcia, David}, date = {2022}, series = {World {{Happiness Report}} 2022}, url = {https://worldhappiness.report/ed/2022/using-social-media-data-to-capture-emotions-before-and-during-covid-19/} }
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Most people now use social media platforms to interact with others, get informed, or simply be entertained. During the COVID-19 pandemic, social lives moved online to a larger extent than ever before, as opportunities for face-to-face social contact in daily life were limited. In this chapter, we focus on what can be learned about people’s emotional experiences and well-being from analyzing text data on social media. Such data is relevant for emotion research, because emotions are not only internal experiences, but often social in nature: Humans communicate their emotions in either verbal or nonverbal ways, including spoken and written language, tone of voice, facial expressions, body postures and other behaviors. Emotions are often triggered by social events: we are sad when we miss someone, happy when we meet loved ones, or angry when someone disappoints us. Emotions also provide important social signals for others, informing them of adaptive ways to interact given their own motivation and goals. Given their valuable social function, emotions are regularly shared with other people and thereby influence other people’s emotions. For instance, happiness may spread through social networks, and give rise to clusters of happy and unhappy people.

Savela, N., Garcia, D., Pellert, M., & Oksanen, A. (2021). Emotional talk about robotic technologies on Reddit: Sentiment analysis of life domains, motives, and temporal themes. New Media & Society, 146144482110672. https://doi.org/10.1177/14614448211067259

@article{savelaEmotionalTalkRobotic2021, title = {Emotional Talk about Robotic Technologies on {{Reddit}}: {{Sentiment}} Analysis of Life Domains, Motives, and Temporal Themes}, shorttitle = {Emotional Talk about Robotic Technologies on {{Reddit}}}, author = {Savela, Nina and Garcia, David and Pellert, Max and Oksanen, Atte}, year = {2021}, month = dec, journal = {New Media & Society}, pages = {146144482110672}, issn = {1461-4448, 1461-7315}, doi = {10.1177/14614448211067259}, abstract = {This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot ( N = 3,433,554), AI ( N = 2,821,614), automation ( N = 879,092), bot ( N = 21,559,939), intelligent agent ( N = 15,119), and software agent ( N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.}, langid = {english} }
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This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot (N = 3,433,554), AI (N = 2,821,614), automation (N = 879,092), bot (N = 21,559,939), intelligent agent (N = 15,119), and software agent (N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.

Garcia, D., Pellert, M., Lasser, J., & Metzler, H. (2021). Social media emotion macroscopes reflect emotional experiences in society at large. ArXiv:2107.13236 [Cs]. http://arxiv.org/abs/2107.13236

@article{garciaSocialMediaEmotion2021, title = {Social Media Emotion Macroscopes Reflect Emotional Experiences in Society at Large}, author = {Garcia, David and Pellert, Max and Lasser, Jana and Metzler, Hannah}, year = {2021}, month = jul, journal = {arXiv:2107.13236 [cs]}, eprint = {2107.13236}, eprinttype = {arxiv}, primaryclass = {cs}, abstract = {Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences. Millions of texts from social media can be processed with computational methods to study emotions over time and across regions. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level and between static regional aggregates of social media emotion and subjective well-being at the population level, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness and algorithmic confounding, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users.}, archiveprefix = {arXiv}, keywords = {Computer Science - Computers and Society,Computer Science - Social and Information Networks} }
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Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences. Millions of texts from social media can be processed with computational methods to study emotions over time and across regions. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level and between static regional aggregates of social media emotion and subjective well-being at the population level, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness and algorithmic confounding, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users.

Savela, N., Oksanen, A., Pellert, M., & Garcia, D. (2021). Emotional reactions to robot colleagues in a role-playing experiment. International Journal of Information Management, 60, 102361. https://doi.org/10.1016/j.ijinfomgt.2021.102361

@article{savelaEmotionalReactionsRobot2021, title = {Emotional reactions to robot colleagues in a role-playing experiment}, volume = {60}, issn = {02684012}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0268401221000542}, doi = {10.1016/j.ijinfomgt.2021.102361}, language = {en}, urldate = {2021-05-27}, journal = {International Journal of Information Management}, author = {Savela, Nina and Oksanen, Atte and Pellert, Max and Garcia, David}, month = oct, year = {2021}, pages = {102361} }
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We investigated how people react emotionally to working with robots in three scenario-based role-playing survey experiments collected in 2019 and 2020 from the United States (Study 1: N = 1003; Study 2: N = 969, Study 3: N = 1059). Participants were randomly assigned to groups and asked to write a short post about a scenario in which we manipulated the number of robot teammates or the size of the social group (work team vs. organization). Emotional content of the corpora was measured using six sentiment analysis tools, and socio-demographic and other factors were assessed through survey questions and LIWC lexicons and further analyzed in Study 4. The results showed that people are less enthusiastic about working with robots than with humans. Our findings suggest these more negative reactions stem from feelings of oddity in an unusual situationand the lack of social interaction.

Di Natale, A., Pellert, M., & Garcia, D. (2021). Colexification Networks Encode Affective Meaning. Affective Science. https://doi.org/10.1007/s42761-021-00033-1

@article{dinataleColexificationNetworksEncode2021, title = {Colexification {Networks} {Encode} {Affective} {Meaning}}, issn = {2662-2041, 2662-205X}, url = {https://link.springer.com/10.1007/s42761-021-00033-1}, doi = {10.1007/s42761-021-00033-1}, abstract = {Abstract Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.}, language = {en}, urldate = {2021-05-17}, journal = {Affective Science}, author = {Di Natale, Anna and Pellert, Max and Garcia, David}, month = may, year = {2021} }
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Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.

Pellert, M., Schweighofer, S., & Garcia, D. (2021). Social Media Data in Affective Science. In U. Engel, A. Quan-Haase, S. X. Liu, & L. Lyberg (Eds.), Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics (1st ed., pp. 240–255). Routledge. https://doi.org/10.4324/9781003024583-18

@incollection{pellertSocialMediaData2021a, title = {Social {{Media Data}} in {{Affective Science}}}, booktitle = {Handbook of {{Computational Social Science}}, {{Volume}} 1: Theory, {{Case Studies}} and {{Ethics}}}, author = {Pellert, Max and Schweighofer, Simon and Garcia, David}, editor = {Engel, Uwe and {Quan-Haase}, Anabel and Liu, Sunny Xun and Lyberg, Lars}, year = {2021}, month = nov, series = {European {{Association}} of {{Methodology Series}}}, edition = {First}, pages = {240–255}, publisher = {{Routledge}}, address = {{London}}, doi = {10.4324/9781003024583-18}, abstract = {“The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors”–}, isbn = {978-1-00-302458-3}, langid = {english}, keywords = {Data processing,Mathematical models,Methodology,Social sciences} }
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The digital traces generated by social media offer the opportunity to analyze human behavior at new scales, depths, and resolutions. The results of analyses of social media data, while sometimes difficult to generalize to a society as a whole, can give important insights on detailed actions and subjective states of individuals. This novel datasource offers a new window to tackle research questions from Affective Science with respect to emotion dynamics, collective emotions, and affective expression in social contexts. In this chapter, we present a balanced view of the benefits, risks, opportunities, and pitfalls of analyzing affective life through social media data. We review a variety of methods to quantify emotions and other affective states from social media data. We illustrate the application of these methods at new scales and resolutions in a series of examples from previous research. We present research gaps and open questions about the role, meaning, and functionality of affective expression in social media, pointing to emerging research trends in computational social science and social psychology. When used critically and with robust research methods, observational analyses of large-scale social media data can be complementary to traditional methodologies in Psychology and Cognitive Science.

Pellert, M., Lasser, J., Metzler, H., & Garcia, D. (2020). Dashboard of sentiment in Austrian social media during COVID-19. Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.00032

@article{pellertDashboardSentimentAustrian2020a, title = {Dashboard of {Sentiment} in {Austrian} {Social} {Media} {During} {COVID}-19}, volume = {3}, issn = {2624-909X}, url = {https://www.frontiersin.org/articles/10.3389/fdata.2020.00032/full}, doi = {10.3389/fdata.2020.00032}, abstract = {To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This enables decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at http://www.mpellert.at/covid19 monitor austria/. Our work is part of an web archive of resources on COVID-19 collected by the Austrian National Library.}, language = {English}, urldate = {2020-10-27}, journal = {Frontiers in Big Data}, author = {Pellert, Max and Lasser, Jana and Metzler, Hannah and Garcia, David}, year = {2020}, note = {Publisher: Frontiers}, keywords = {Affective sciences, Collective emotions, COVID-19, Dashboard, Digital traces, Real-time monitoring, Social Media, Webscraping} }
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To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This enables decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at http://www.mpellert.at/covid19_monitor_austria/. Our work is part of an web archive of resources on COVID-19 collected by the Austrian National Library.

Pellert, M., Schweighofer, S., & Garcia, D. (2020). The individual dynamics of affective expression on social media. EPJ Data Science, 9(1), 1. https://doi.org/10.1140/epjds/s13688-019-0219-3

@article{pellertIndividualDynamicsAffective2020, title = {The individual dynamics of affective expression on social media}, volume = {9}, issn = {2193-1127}, url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0219-3}, doi = {10.1140/epjds/s13688-019-0219-3}, language = {en}, number = {1}, urldate = {2020-01-13}, journal = {EPJ Data Science}, author = {Pellert, Max and Schweighofer, Simon and Garcia, David}, month = dec, year = {2020}, pages = {1} }
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Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.

Trpin, B., & Pellert, M. (2018). Inference to the Best Explanation in Uncertain Evidential Situations. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axy027

@article{trpin_inference_2018, title = {Inference to the Best Explanation in Uncertain Evidential Situations}, issn = {0007-0882, 1464-3537}, url = {https://academic.oup.com/bjps/advance-article/doi/10.1093/bjps/axy027/4935150}, doi = {10.1093/bjps/axy027}, journaltitle = {The British Journal for the Philosophy of Science}, author = {Trpin, Borut and Pellert, Max}, urldate = {2018-03-19}, date = {2018-03-14}, langid = {english} }
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It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by IBE** and JC, respectively, try to detect the bias of a coin while they are only partially certain what side the coin landed on. We show that IBE** more often prescribes high probability to the actual bias than JC. We also show that this happens considerably faster, that IBE** passes higher thresholds for high probability, and that it in general leads to more accurate probability distributions than JC.

References
Douven, I. [2013]: ‘Inference to the Best Explanation, Dutch Books, and Inaccuracy Minimisation’, Philosophical Quarterly, 63(252), pp. 428–44.
Douven, I. and Wenmackers, S. [2017]: ‘Inference to the Best Explanation versus Bayes’s Rule in a Social Setting’, The British Journal for the Philosophy of Science, 68(2), pp. 535–70.

Pellert, M. (2017). Collective Dynamics of Multi-Agent Networks: Simulation Studies in Probabilistic Reasoning. In P. Hochenauer, C. Schreiber, K. Rötzer, & E. Zimmermann (Eds.), Proceedings of the MEi: CogSci Conference 2017. Budapest, Hungary: Comenius University in Bratislava, Slovakia.

@inproceedings{pellert2017, location = {{Budapest, Hungary}}, title = {Collective {{Dynamics}} of {{Multi}}-{{Agent Networks}}: {{Simulation Studies}} in {{Probabilistic Reasoning}}}, isbn = {978-80-223-4325-1}, timestamp = {2017-12-02T18:48:08Z}, booktitle = {Proceedings of the {{MEi}}: {{CogSci Conference}} 2017}, publisher = {{Comenius University in Bratislava, Slovakia}}, author = {Pellert, Max}, editor = {Hochenauer, P. and Schreiber, C. and Rötzer, Katharina and Zimmermann, Elisabeth}, editorb = {Farkas, I.}, editorbtype = {redactor}, date = {2017-06} }
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Agent-based modelling is seen as an alternative to traditional, “equation-based” modelling. It has seen applications in diverse areas. At the same time, the modern formulation of network theory became part of science, influencing discipline after discipline. Historically, however, networks have almost exclusively been dealt with implicitly by agent-based modellers. Their “network awareness” is a very recent phenomenon [1].

Belief updating refers to the process that enables an agent to alter his belief in a given hypothesis conditional on evidence that it receives. This concept is part of the field of “formal epistemology” that explores knowledge and reasoning using tools from math and logic. Bayesian approaches to probabilistic reasoning are dominant here. Proponents uphold that Bayesianism is the only rational way of belief formation, given that no other strategy protects an agent in principle from “Dutch books” (bets that guarantee a loss to one side). Nonetheless, alternatives that are probabilistic but not (Standard-)Bayesian have been introduced under the heading of “Inference to the Best Explanation”. The use of alternatives is justified by questioning the practical relevance of Dutch book arguments and by resorting to pragmatism: It has been shown that there are strategies that have speed and accuracy advantages in belief updating [2].

This thesis will explore different scenarios with multiple agents updating their beliefs and influencing each other through specific network structures. The dynamics on networks as well as the dynamics of networks will therefore be in the focus of analysis [3]. Agent behavior and network dynamics develop interdependently, i.e. they coevolve.

Apart from being a methodological advance, it is expected that this approach can yield novel theoretical insights on the dynamical formation of network structures by interacting agents. Additionally, it will be possible to demonstrate that there are alternatives to Bayesian updating that also yield advantages in multi-agent settings. On a meta-theoretical level it will be argued that simulation results can be treated as quasi-empirical. Several necessary conditions will be identified that need to be fulfilled for this aim.
We borrow methods from computer science to investigate questions from philosophy (of science). Additionally, work done in the social sciences on agents and networks will provide input.

References
[1] M. Newman, Networks: An Introduction. New York, NY, USA: Oxford University Press, Inc., 2010.
[2] I. Douven and S. Wenmackers, “Inference to the Best Explanation versus Bayes’s Rule in a Social Setting,” The British Journal for the Philosophy of Science, 2015.
[3] A. Namatame and S.-H. Chen, Agent-Based Modeling and Network Dynamics. Oxford, New York: Oxford University Press, p.10, 2016.

Pellert, M. (2016). Are Heterogeneous Expectations a Viable Alternative to Rational Expectations in Economics? In S. Ersoy, P. Hochenauer, & C. Schreiber (Eds.), Proceedings of the MEi: CogSci Conference 2016. Vienna, Austria: Comenius University in Bratislava, Slovakia.

@inproceedings{pellert2016, location = {{Vienna, Austria}}, title = {Are {{Heterogeneous Expectations}} a {{Viable Alternative}} to {{Rational Expectations}} in {{Economics}}?}, isbn = {978-80-223-4137-0}, timestamp = {2017-07-24T16:54:42Z}, booktitle = {Proceedings of the {{MEi}}: {{CogSci Conference}} 2016}, publisher = {{Comenius University in Bratislava, Slovakia}}, author = {Pellert, Max}, editor = {Ersoy, S. and Hochenauer, P. and Schreiber, C.}, editorb = {Farkas, I.}, editorbtype = {redactor}, date = {2016} }
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Expectations play a crucial role in economic theories. Different ways of modelling and parameterising expectations change outcomes massively in (macro-)economic models. On a meta-theoretical level, some authors even go as far as to claim that: “Individual expectations about future aggregate outcomes are the key feature that distinguishes social sciences and economics from the natural sciences” [1].

The prevailing expectation hypothesis used in economics is that of “rational expectations” (RE) [2]. Here, it is assumed that expectations are nothing more than the predictions of the relevant theory, i.e., they are defined to be model-consistent. Furthermore, it is assumed that people make no systematic mistakes. Finally, modellers typically introduce “representative agents” (e.g., “the average household”) equipped with RE to bridge the gap between micro- and macro-levels; all interacting units of the economy are therefore alike. Over time, it became more and more evident that certain real-world phenomena, like the global financial crisis, are very hard to fit in a uniform RE framework.

Expectations modelling in economic theory thus is in need of theoretical innovation. New proposals ought to have a strong interdisciplinary foundation, reconciling valuable insights from several other disciplines, including sociology, psychology and anthropology. In particular, we take interdisciplinary thinking to be an effective remedy against the harmful tendency of a large part of economics to isolate specific aspects of human behaviour and then to treat these and only these as “purely” economic subject matter, as in the case of RE.

The concept of “heterogeneous expectations” has been suggested as an alternative to RE, instantiated in so-called “agent-based models” [3]. Here, heterogeneous agents use different methods of expectation formation. The “wilderness” of this approach is fenced in e.g. by the use of genetic algorithms that describe agents' switching between heuristics according to a fitness criterion, e.g. accuracy of prediction.

The goal of our project is to give a critical assessment of rewarding ends and eventual dead ends in the theoretical development of agent-based economic modelling with heterogeneous expectations. The outcome of this work should be a stepping stone towards a more outward-looking theory of dynamics in (financial) markets.

Acknowledgments
Special thanks to Paolo Petta and OFAI for supporting this project.

References
[1] C. Hommes, “The heterogeneous expectations hypothesis: Some evidence from the lab,” Journal of Economic Dynamics and Control, vol. 35, no. 1, 2011, pp.1.
[2] J. F. Muth, “Rational Expectations and the Theory of Price Movements,” Econometrica, vol. 29, no. 3, pp. 315–335, 1961.
[3] D. Delli Gatti, S. Desiderio, E. Gaffeo, P. Cirillo and M. Gallegati, Macroeconomics from the Bottom-up. Milan, Italy: Springer Italia, 2011.