Ellis Alicante in Spain and the Dynamic Decision Making Laboratory (DDMLab) in the USA, have joined forces to create research collaborations on Human-Centered Artificial Intelligence (AI).
In the 2023 Ellis-DDMLab 3-day workshop, researchers from both laboratories will come together to encourage and develop novel collaborations in the study of human-AI complementarity and the role of AI in human societies. This workshop will be held March 7-9, 2023 in Alicante, Spain.
The goal of this workshop is to generate new research ideas that will advance the foundational research of human-AI collaborations and interdependencies. At the end of this workshop, researchers from Ellis and DDMLab would have determined concrete themes of collaboration between the two laboratories, which will be pursued in future search.
There is growing interest in interdisciplinary understanding of the anticipatory processes regarding other people's actions and also one's own actions. This surge in interest is especially important given that anticipatory interaction is needed in the growing area of intelligent systems that work closely together with human partners. Decades of research on human-machine interaction (HMI) have resulted in significant advances in theories, tools, and methods to facilitate, support, and enhance interactions between humans and computing systems. Despite the fundamental importance of HMI for our information society and numerous advances towards making interactions with machines more human-like, current systems still fall short of human ability. With this seminar we have made a first step in bridging this gap and have discussed theoretical foundations, key research challenges and opportunities, new computational methods, as well as applications and use cases of anticipatory HMI.
A theoretical distinction has emerged in the past decade regarding how decisions are made from description (explicit definition of risks, outcomes, probabilities) or experience (implicit collection of past outcomes and probabilities). Explanations of choice under descriptive information often rely on Prospect Theory, while experiential choice has been plagued by highly task-specific models that often predict choice in particular tasks but fail to explain behavior even in closely related tasks. Furthermore, in social interactions the information about others (preferences, beliefs, degree of interdependence) may also influence their interactions and choice, but narrow self-interest and complete information is a common assumption in empirical game theory paradigms, limiting our understanding of the types of uncertainty that people face in real-world social interactions, especially when cooperation is welfare enhancing. The goal of this workshop is to bring recent research that crosses the borders of traditional descriptive or experiential approaches and attempts to address decision making where many levels of information are available.
This workshop was sponsored by the Center for Formal Epistemology, which is based on the philosophy department at Carnegie Mellon University. The department has a long tradition of interdisciplinary work in bounded rationality, heuristics, and choice. One of the ideas of this workshop was to celebrate the memory and the work on Herb Simon, who was an active member of the department and the CMU community. Various members of the department remain interested in the program of bounded rationality that Simon proposed initially and this workshop continued work in this direction.
Participants of this challenge were invited to develop computational models that simulate human performance on the dynamic stocks and flows (DSF) task in a variety of conditions. The goal was not to produce a model of optimal behavior but, rather, a model that can predict actual behavior of human participants, including mistakes and limitations, as they learn to control the DSF. Moreover, modelers only had access to a subset of the observed data in developing their model, meaning that the predictions made by a model must generalize to new conditions that will test the model’s generality and scalability as a function of task complexity.
The first Latin American Conference on Human-Computer Interaction, CLIHC 2003, was a forum for the exchange of ideas, developments and research findings in Human-Computer Interaction (HCI). The main goal of the conference was to foster communication and collaboration among HCI researchers and professionals from countries in Latin America and to consolidate the presence of the Latin American HCI community abroad. For this, we encouraged members of other communities to come to the conference, and discuss strategies for Latin Americans to have a wider participation internationally.