Human decision making is central in many functions across a broad spectrumof fields including marketing, investments and smart contracts, digital health,political campaigns, logistics, and strategic management to name only a few.Computational behavioral science, the focus of the proposed workshop, notonly studies the various psychological, cultural, and social factors contributingto decision making besides reasoning, but it also seeks to construct robust,scalable, and efficient computational models imitating or extending this way ofdecision making. It should be highlighted here that computational behavioralscience does not negate the rationality axioms of classical economic theorybut rather extends them. Computational behavioral science can evaluate theindividual or collective decision making processes in massive populations withsignal estimation or deep learning techniques based on a wide array ofattributes ranging from social media posts and multimedia to physiologicalsigns and neuroimaging results. Additionally, time dependent decision makingprocesses can be understood and processed in a signal processing contextand even tracked with input-output or state space models. As dispositiontowards alternative decisions may well change over time, this is a majoradvantage compared to traditional decision making analysis. So far the primaryfindings in the field are concepts like bounded rationality and perceived risk,while results include optimal strategies for various levels of informationawareness and action strategies based on perceived loss aversion principleswhich have been successfully applied to many situations. THECOG for asecond continuous year will be a central meeting point for researchers forgenerating new interdisciplinary and groundbreaking results.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full papers
• Short papers
• Posters

List of Topics

• Affective state in social media beyond emotional polarity​
• Behavioral economics and deep learning​
• Bounded rationality estimation techniques and computational models
• Case​ ​​​​​​​​​​​studies of behavioral economics
• Computational affective models
• Data-driven approaches for strategy recommendation
• Deep learning strategies for training nudge theory models
• Distributed approaches to computing public sentiment at massive scale
• Graph neural networks for assessing sentiments in social graphs
• Higher order sentiment metrics
• Information augmentation through social media and crowdsourcing
• Semantics for Barnum statement and affective state discovery
• Natural language processing for aspect mining
• Nudge theory based strategies for brand loyalty and social media campaigns
• Risk estimators based on perceptive criteria and incomplete information

Committees

Program Committee

• Prof. Eleanna Kafeza, Zayed University of Technology
• Dr. Georgios Drakopoulos, Ionian University

Organizing committee

• Prof. Phivos Mylonas, University of West Attica
• Prof. Spyros Sioutas, University of Patras
• Prof. Kostas Tsichlas, University of Patras

Invited Speakers

• TBA

Publication

TBA

Contact

TBA