From Bruno.Emond at nrc-cnrc.gc.ca Wed Aug 9 15:06:07 2017 From: Bruno.Emond at nrc-cnrc.gc.ca (Emond, Bruno) Date: Wed, 9 Aug 2017 15:06:07 -0400 Subject: [ACT-R-users] Machine learning positions at the National Research Council Canada Message-ID: Four research officer positions at the National Research Council Canada. >From the job announcement: "The primary responsibility of the researcher in this position is to support the goals of NRC and the activities of the Information and Communications Technologies (ICT) Portfolio in conducting research of international calibre in machine learning, and the development and application of modern machine learning methods to computer vision, natural language processing, artificial intelligence, cyber-security, life sciences, engineering or manufacturing. The researcher will work in a team environment with other researchers and technical experts in world-class facilities. The researcher will be called on to participate in international evaluations or demonstrations of the team?s applied technologies.? See the attachement for more details and contact information. Bruno -- Bruno Emond, Ph.D. Senior Research Officer | Agent de Recherches Senior Human-Computer Interaction | Interaction personne-machine Information and Communications Technologies | Technologies de l?information et des communications National Research Council Canada | Conseil National de Recherches Canada Government of Canada | Gouvernement du Canada 1200 Chemin Montr?al, M50, Ottawa, ON K1A 0R6 T:1.613.993.0154 F:1.613.952.0215 bruno.emond at nrc-cnrc.gc.ca -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Final Poster_121-17-0322.pdf Type: application/pdf Size: 219332 bytes Desc: Final Poster_121-17-0322.pdf URL: From Bruno.Emond at nrc-cnrc.gc.ca Wed Aug 9 15:49:59 2017 From: Bruno.Emond at nrc-cnrc.gc.ca (Emond, Bruno) Date: Wed, 9 Aug 2017 15:49:59 -0400 Subject: [ACT-R-users] Machine learning positions at the National Research Council Canada Message-ID: Four research officer positions at the National Research Council Canada. >From the job announcement: "The primary responsibility of the researcher in this position is to support the goals of NRC and the activities of the Information and Communications Technologies (ICT) Portfolio in conducting research of international calibre in machine learning, and the development and application of modern machine learning methods to computer vision, natural language processing, artificial intelligence, cyber-security, life sciences, engineering or manufacturing. The researcher will work in a team environment with other researchers and technical experts in world-class facilities. The researcher will be called on to participate in international evaluations or demonstrations of the team?s applied technologies.? See the following link for more details and contact information. http://www.nrc-cnrc.gc.ca/careers/jobpost.nsf/EnglishAll/A6B2F3C7DB7453EA852581630053996B Bruno -- Bruno Emond, Ph.D. Senior Research Officer | Agent de Recherches Senior Human-Computer Interaction | Interaction personne-machine Information and Communications Technologies | Technologies de l?information et des communications National Research Council Canada | Conseil National de Recherches Canada Government of Canada | Gouvernement du Canada 1200 Chemin Montr?al, M50, Ottawa, ON K1A 0R6 T:1.613.993.0154 F:1.613.952.0215 bruno.emond at nrc-cnrc.gc.ca -------------- next part -------------- An HTML attachment was scrubbed... URL: From julian.marewski at unil.ch Mon Aug 14 07:45:40 2017 From: julian.marewski at unil.ch (Julian Marewski) Date: Mon, 14 Aug 2017 11:45:40 +0000 Subject: [ACT-R-users] Doctoral student position on heuristic decision making, with a potential focus on swarm and other group behavior -- University of Lausanne, Switzerland Message-ID: <8994eb63cea24d32b96c25a4f1af6119@prdexch06.ad.unil.ch> Apologies for cross-postings. Doctoral student position on heuristic decision making, with a potential focus on swarm and other group behavior -- University of Lausanne, Switzerland The focus of our interdisciplinary research program is on studying how people make decisions under uncertainty based on a repertoire of simple rules of thumb, so called fast-and-frugal heuristics. We investigate how such heuristics can exploit both the structure of environments and the workings of basic cognitive capacities, such as memory. In doing so, we model the cognitive processes underlying bounded rationality in a wide range of social and non-social environments. For instance, we have been interested in how social environments can induce people to commit both unethical and laudable acts, be it in (e.g., historical) military or (e.g., current-day) industry contexts. To offer another example, we have studied how the human memory system exploits statistical regularities in social and non-social environments and how that interplay between memory and the environment, in turn, influences how people make decisions with the so-called recognition and fluency heuristics. In conducting such research, we reach out to different disciplines, including psychology, computer science, biology, and history, to name but a few. In terms of research methodology, the work carried out in our group draws on experimental methods, mathematical and computational cognitive models, and agent-based models. The theoretical background is the fast-and-frugal heuristic research program to decision making, developed originally at the Max Planck Institute for Human Development in Berlin, Germany (www.mpib-berlin.mpg.de/en/research/adaptive-behavior-and-cognition). Much of our cognitive modeling work (e.g., of memory) makes use of the ACT-R architecture (http://act-r.psy.cmu.edu/). Applicants should be interested in the computational or mathematical modeling of heuristic decision making processes. Prior exposure to research on heuristics is not required. Knowledge of experimental methods, prior exposure to quantitative research methods (e.g., cognitive modeling, agent-based modeling), and programming skills (e.g., MATLAB, R, LISP) are helpful but not required. A university degree in psychology, mathematics, computer science, physics, biology, business, economics, history or another discipline is required. Excellent English skills are required. Applicants should be interested in pursuing a career in academia. We explicitly encourage applications from candidates who would be interested in investigating how individuals make decisions in groups (e.g., modeling human swarm behavior); but also proposals for any other interesting research project will be considered. The doctoral student position (60%) can begin as early as Nov 1st in 2017, or on a date mutually agreed upon. The maximum funding period is 5 years, with the first contract being 1 year and then renewable 2X2 years. Successful candidates will obtain a Ph.D. The doctoral student will be mentored by Julian Marewski. The work location is Lausanne Dorigny. Please submit applications by September 10th, but the job offer will remain open until the position is filled. Applications include a cover letter describing past research experience and a detailed outline of 3 research projects the candidate would be interested in pursuing as part of his/her doctoral work. A curriculum vitae, university transcripts, two letters of recommendation, and -- if existing -- publications should be included, too. The preferred method of submission is PDF files e-mailed to julian.marewski[at]unil.ch. Letters of recommendation can be submitted at a later point in time, to be agreed upon with Julian Marewski. The Department of Organizational Behavior of the Faculty for Business and Economics at the University of Lausanne provides a stimulating, interdisciplinary research environment. At the department, professorial faculty are Ulrich Hoffrage, Joerg Dietz, John Antonakis, Franciska Krings, Marianne Schmid Mast, Christian Zehnder, and Julian Marewski. We value the diversity of the expertise of the members of our department (department members come from diverse fields, ranging from the cognitive and decision sciences to behavioral economics, mathematics, and physics; we have Ph.D.s in psychology, business, management, and economics). We publish in top-tier journals in different disciplines, including Science, Psychological Review, Behavioral and Brain Sciences, and the American Economic Review. The working language of our department is English. Located near Lake Geneva and surrounded by the Jura Mountains and the French Alps, Lausanne is a beautiful and cosmopolitan spot to live. More information about the position can be inquired directly from Julian.marewski[at]unil.ch. This is not an official job announcement or job description from the University of Lausanne. -------------- next part -------------- An HTML attachment was scrubbed... URL: From julian.marewski at unil.ch Mon Aug 14 07:37:14 2017 From: julian.marewski at unil.ch (Julian Marewski) Date: Mon, 14 Aug 2017 11:37:14 +0000 Subject: [ACT-R-users] Doctoral student position on heuristic decision making, with a potential focus on swarm and other group behavior -- University of Lausanne, Switzerland In-Reply-To: <1502710567110.91230@unil.ch> References: <1502710567110.91230@unil.ch> Message-ID: <1502710638609.27964@unil.ch> Apologies for cross-postings. Doctoral student position on heuristic decision making, with a potential focus on swarm and other group behavior -- University of Lausanne, Switzerland The focus of our interdisciplinary research program is on studying how people make decisions under uncertainty based on a repertoire of simple rules of thumb, so called fast-and-frugal heuristics. We investigate how such heuristics can exploit both the structure of environments and the workings of basic cognitive capacities, such as memory. In doing so, we model the cognitive processes underlying bounded rationality in a wide range of social and non-social environments. For instance, we have been interested in how social environments can induce people to commit both unethical and laudable acts, be it in (e.g., historical) military or (e.g., current-day) industry contexts. To offer another example, we have studied how the human memory system exploits statistical regularities in social and non-social environments and how that interplay between memory and the environment, in turn, influences how people make decisions with the so-called recognition and fluency heuristics. In conducting such research, we reach out to different disciplines, including psychology, computer science, biology, and history, to name but a few. In terms of research methodology, the work carried out in our group draws on experimental methods, mathematical and computational cognitive models, and agent-based models. The theoretical background is the fast-and-frugal heuristic research program to decision making, developed originally at the Max Planck Institute for Human Development in Berlin, Germany (www.mpib-berlin.mpg.de/en/research/adaptive-behavior-and-cognition). Much of our cognitive modeling work (e.g., of memory) makes use of the ACT-R architecture (http://act-r.psy.cmu.edu/). Applicants should be interested in the computational or mathematical modeling of heuristic decision making processes. Prior exposure to research on heuristics is not required. Knowledge of experimental methods, prior exposure to quantitative research methods (e.g., cognitive modeling, agent-based modeling), and programming skills (e.g., MATLAB, R, LISP) are helpful but not required. A university degree in psychology, mathematics, computer science, physics, biology, business, economics, history or another discipline is required. Excellent English skills are required. Applicants should be interested in pursuing a career in academia. We explicitly encourage applications from candidates who would be interested in investigating how individuals make decisions in groups (e.g., modeling human swarm behavior); but also proposals for any other interesting research project will be considered. The doctoral student position (60%) can begin as early as Nov 1st in 2017, or on a date mutually agreed upon. The maximum funding period is 5 years, with the first contract being 1 year and then renewable 2X2 years. Successful candidates will obtain a Ph.D. The doctoral student will be mentored by Julian Marewski. The work location is Lausanne Dorigny. Please submit applications by September 10th, but the job offer will remain open until the position is filled. Applications include a cover letter describing past research experience and a detailed outline of 3 research projects the candidate would be interested in pursuing as part of his/her doctoral work. A curriculum vitae, university transcripts, two letters of recommendation, and -- if existing -- publications should be included, too. The preferred method of submission is PDF files e-mailed to julian.marewski[at]unil.ch. Letters of recommendation can be submitted at a later point in time, to be agreed upon with Julian Marewski. The Department of Organizational Behavior of the Faculty for Business and Economics at the University of Lausanne provides a stimulating, interdisciplinary research environment. At the department, professorial faculty are Ulrich Hoffrage, Joerg Dietz, John Antonakis, Franciska Krings, Marianne Schmid Mast, Christian Zehnder, and Julian Marewski. We value the diversity of the expertise of the members of our department (department members come from diverse fields, ranging from the cognitive and decision sciences to behavioral economics, mathematics, and physics; we have Ph.D.s in psychology, business, management, and economics). We publish in top-tier journals in different disciplines, including Science, Psychological Review, Behavioral and Brain Sciences, and the American Economic Review. The working language of our department is English. Located near Lake Geneva and surrounded by the Jura Mountains and the French Alps, Lausanne is a beautiful and cosmopolitan spot to live. More information about the position can be inquired directly from Julian.marewski[at]unil.ch. This is not an official job announcement or job description from the University of Lausanne. -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.sauerwald at fernuni-hagen.de Tue Aug 29 23:51:47 2017 From: kai.sauerwald at fernuni-hagen.de (Kai Sauerwald) Date: Wed, 30 Aug 2017 05:51:47 +0200 Subject: [ACT-R-users] How do I archive probabilistic production rule selection? Message-ID: <8d6a142d-2922-9919-e7b4-74e012889039@fernuni-hagen.de> Hello, is there a way to archive probabilistic selection of production rules in ACT-R? More concrete:? suppose two productions p1 and p2 with the same condition, how do I have to setup the rules and ACT-R such that p1 is selected with probability 0.3 and p2 selected with probability 0.7 in conflict resolution? Thank you! Kai Sauerwald -- University of Hagen From db30 at andrew.cmu.edu Wed Aug 30 08:58:16 2017 From: db30 at andrew.cmu.edu (db30 at andrew.cmu.edu) Date: Wed, 30 Aug 2017 08:58:16 -0400 Subject: [ACT-R-users] How do I archive probabilistic production rule selection? In-Reply-To: <8d6a142d-2922-9919-e7b4-74e012889039@fernuni-hagen.de> References: <8d6a142d-2922-9919-e7b4-74e012889039@fernuni-hagen.de> Message-ID: <6C6BB266F368BD4361DA06E8@actr6b.psy.cmu.edu> --On Tuesday, August 29, 2017 11:51 PM -0400 Kai Sauerwald wrote: > Hello, > > is there a way to archive probabilistic selection of production rules in > ACT-R? > > More concrete:? suppose two productions p1 and p2 with the same condition, > how do I have to setup the rules and ACT-R such that p1 is selected with > probability 0.3 and p2 selected with probability 0.7 in conflict resolution? > > To do that you would need to set the utilities of those productions and the utility noise value. The equation that describes the probability of choosing a production based on its utility, the utility of the competing productions, and the utility noise is shown in unit 6 of the ACT-R tutorial. Unit 3 of the tutorial shows how to set fixed utility values using the spp command, and the parameter for utility noise is :egs as shown in unit 6. Unit 6 also describes the process through which a model can learn those utility values based on the rewards it is given. Hope that helps, Dan