Shikakeology: Designing Triggers for Behavior Change, AAAI 2013 Spring Symposium Series

Shikakeology: Designing Triggers for Behavior Change

AAAI 2013 Spring Symposium Series
March 25-27, 2013 at Stanford University, Stanford, California.

Organizers: Naohiro Matsumura (Osaka University), Renate Fruchter (Stanford University)



How do you trigger learning by seeing?
How do you encourage eco-conscious behaviors?
How do you trigger health awareness?
How do you encourage crime prevention?

Shikake is a Japanese word that represents physical and/or psychological trigger for implicit or explicit behavior change to solve problems. The aim of this workshop is to gain a holistic understanding of Shikake, i.e.:

The merits of Shikakeological approach are summarized as four points; low expertise, low cost, wide range of target users, and long term continuous behavior changes. Developing a Shikake can be easier and less expensive than developing complicated engineering mechanism. These advantages allow people to use the Shikake approach to address immediate problems without requiring specific expertise.

Another Shikake objective is to induce spontaneous behavior. When people feel controlled or forced by someone or something to do something, they never do that again. On the other hand, if people desire and enjoy changing their behavior, they would do it repeatedly. Shikake aims to change behavior through a continuous engagement and transformation process.

The goal of Shikakeology is to codify the cause and effect of Shikake cases from physical and/or psychological points of view, and to establish a Shikake design methodology. To achieve this goal, this workshop invites Shikake studies to share the knowledge, methods, experiments and findings that demonstrate triggers that motivate people and lead to behavior changes.

The fundamental research of Shikaleology is deeply related to Artificial Intelligence topics through: 1) interaction design of embodied-, situated-, and behavior-based intelligence, 2) definition of Shikake ontology and knowledge representation; 3) codifying Shikake cause and effect, and 4) formalizing intelligent and adaptive Shikake algorithms for reasoning, planning, and learning. Shikake can be viewed as an intelligence amplifier for new Artificial Intelligence platforms.


We are surrounded by various things that are visible and/or hearable but not being recognized. However, a Shikake can make people aware of them. A cylinder in the below pictures is located beside a pathway at Tennouji Zoo in Japan. By observing the passers-by near the cylinder, we found that many people, especially kids, were interested in, approached to, and looked into the cylinder, and eventually found something interesting beyond the cylinder and enjoyed the discoveries. The looking people themselves also becomes a trigger to attract others.

This is an example of easy, primitive, and cheap but powerful Shikake. Carefully looking around the world, we can find many Shikake examples that change our consciousness and behavior.


The workshop aims to bring together researchers and practitioners who focus on Shikake design. We expect to encourage and exchange ideas and perceptions through the workshop. Topics of interest include, but are not limited to:

We invite papers that present new approaches to behavior change which are related or contribute to the study of Shikake. Case studies of behavior change and innovative new Shikake concepts are welcome. The evaluation criteria of submitted papers will include: statistical verification, creativity, and applicability.



Naohiro Matsumura
Graduate School of Economics, Osaka University
1-7 Machikaneyama, Toyonaka, Osaka, 560-0043 JAPAN

Renate Fruchter
PBL Lab, Department of Civil and Environmental Engineering
Stanford University
Stanford, CA 94305


Akiko Orita (Keio University)
Asako Miura (Kwansei Gakuin University)
Chikahiro Hanamura (Osaka Prefecture University)
Hikaru Yamamoto (NYU / Seikei University)
Kumiyo Nakakoji (Software Research Associates, Inc.)
Mark Nelson (Stanford University)
Masaki Suwa (Keio University)
Mitsunori Matsushita (Kansai University)
Toyoaki Nishida (Kyoto University)
Yukio Ohsawa (The University of Tokyo)



Interested participants may submit your extended abstracts - more than 400 words in PDF format - to .

AAAI will sends the authors of accepted papers the instructions on how and when to prepare the technical reports notes. AAAI symposium authors are required to use the AAAI style files to prepare their papers ( Electronic versions of abstracts and papers will be asked to submit via the AAAI web site (the site will be announced later from AAAI).

Note: the symposium papers will be part of the AAAI Technical Report Series.