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 Handbook of Computational Social Science Volume 1: Theory, Case Studies and Ethics. forthcoming.

@incollection{pellertSocialMediaData2021, title = {Social {Media} {Data} in {Affective} {Science}}, booktitle = {Handbook of {Computational} {Social} {Science} {Volume} 1: {Theory}, {Case} {Studies} and {Ethics}}, publisher = {forthcoming}, author = {Pellert, Max and Schweighofer, Simon and Garcia, David}, year = {2021} }
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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.