Solving Problems Beyond
Problem Solving
Intrinsic Motivations in Techno-Social Systems
Rosaria Conte, LABSS, ISTC-CNR
FET Information Day
Brussels, January 20th, 2014
By PresenterMedia.com
Overjustification:
Irrationality or Nonstrategic Rationality?
Over-justification: incentives may hinder performance
Learning (Deci et al. 1971)
The case of blood donation (Titmuss,1970, Lacetera et al. 2013)
Money collection (Gneezy and Rustichini 2000)
Intrinsic Motivations (IM): activities performed for inherent satisfaction or
pleasure. (Brown, 2007)
Properties
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Stable and dispositional
Hard to resist
Relatively incompatible with incentives and strategic planning
Beyond problem solving

To shape the long-term development
of individual and social knowledge
and identities
Enjoyment

To design sociotechnical systems that
can use this kind of
motivations as a
leverage
Inherent
Satisfaction
Autonomy

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To understand highly
heterogeneous and
dynamic settings in
order to foster
inclusive, diverse,
multicultural social
environments
INTRINSIC
MOTIVATION
Interest
Competence
To enlight the difference between incentives and internal motivations
to develop policy recommendations and to design ICT tools for
addressing issues like urban change, migration, social and
gender divides, multiculturalism, inter-disciplinarity
Examples: the thrill of gossip; fairness in resource distribution; motivation to learn;
job effort; voluntary contribution
Scientific challenges
What is the relationship between intrinsic (including conflicts)
and extrinsic motivations, like incentives (including IM and
drives)?
How is intrinsic motivation related to automatic behavior,
internalization and incentives?
What is their relationship with cultural values?

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Effects at different time scales
Variability and dynamics
What is their role in identity formation, both social and
individual?

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How do humans and groups solve conflicts among different kinds of
intrinsic motivations (e.g., thrill of gossip vs. negative reputation of
gossipers)?
How to promote pro-social and how to rule anti-social IM (policy making)?
Technological challenges
Can technosocial systems
exploit tuning
of intrinsic vs.
extrinsic
motivation in
different
scenarios?
“SENSORS”
(behavioural
correlates of IM)
INDIVIDUAL
SOCIAL
“Diagnostic”
Updating
Collective
Mood and Motivation
Detector
Dynamic
Tutoring
Dynamic Policy
Advisor
BEING
“ACTUATORS”
(simulation-based)
To allow for such a tuning, we need at the individual and collective
levels:


Sensors: individualized and theory-driven detection of IM through
self-assessment and emotional reading
Actutators: tunable regulations
Examples
INDIVIDUAL
Emotional
State
“Sensor”
Dynamic
Tutoring
SOCIAL
Collective
Mood and
Motivation
Detector
Dynamic
Advisor
Application scenarios
Could benefit from initial extrinsic motivation but should be designed
to stimulate intrinsic motivations

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Bad habits: coping with
Diversity and urban change: motivations for identity-based inclusion and
sharing (public spaces vs. segregation)
Intrinsic motivations at the base of migration choices
Social divide: gentrification. Extrinsic motivation to recover urban areas that are
later colonized by wealthy people and reproduce exclusion
Hooliganism: how intrinsic motivation generates irrational (violent)
behavior
Intelligent tutoring within and behind official education
Automated markup/reaction to shifts in motivational status (to address truancy)
Waste sorting
Intrinsic motivations for
overcoming vertical
differences and designing
ICT tools and
infrastructures for inclusive,
multi-cultural and dynamic
societies
Information
provision
through ICT
tools (e.g.
Tripadvisor;
Reputation
management)
Inclusive urban
settings design
in which spatial
distance can
be reduced
through ICTenabled
cultural
proximity
Methods
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Experimental studies (in and out of the
laboratory)
Virtual chat, online experiments
Big data collection and analysis
Analysis of social networks dynamics as an
indicator of motivation
Agent-based simulation
Connecting theory and individual behavior
Hybrid experiments
social street, virtual communities, time banks,
barter communities, ethical purchasing groups
Competencies needed:
Cognitive Science:
Psychology,
Neuroscience
Artificial Intelligence:
Exp. Economics
Agent based Social Simulation
Sociology:
Complex Systems:Science
Big Data Science
Techno-social systems design
Social data mining,
Thank you!
Rosaria Conte, LABSS, ISTC-CNR
FET Information Day
Brussels, January 20th, 2014
By PresenterMedia.com