Ridesharing

from the simple matching scenarioto the real world application

Sofia Ceppi

Michael Rovatsos Pavlos Andreadis

Andy

Betty

Charles

Diana

Edward

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Andy

Betty

Charles

Diana

Edward

from Edinburgh to Glasgow

Uncertainty due to

human behaviour

Uncertainty due to

human behaviour

imposing a solutionrecommending

a set of solutions

Recommending a set of solutions: Problems

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

Andy Betty Charles

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

Andy Betty Charles

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

Andy Betty Charles

1) “optimal” set of solutions

2) influence users choices

3) How to learn users preferences

Recommending a set of solutions: Problems

Andy Betty Charles

Single User and Collective of Users

Single User and Collective of Users

User's utility function: - how good a solution is for the user- depends on user's requirements and preferences

System's utility function:- depends on social welfare- other factors

Single User and Collective of Users

User's utility function: - how good a solution is for the user- depends on user's requirements and preferences

System's utility function:- depends on social welfare- other factors

Set of Solutions Influence the Users Learning

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Constraints feasibility

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Constraints feasibility

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Constraints feasibility

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Constraints

Constraints

feasibility

MIP*

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Constraints

Constraints

feasibility

MIP*

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Objective

Constraints

Constraints

feasibility

MIP*

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Objective

Constraints

Constraints

Constraints

feasibility

MIP*

MIPfirst

MIPfirst

MIPothers

MIP*

Set of Solutions Influence the Users Learning

Objective

Objective

Objective

Constraints

Constraints

Constraints

feasibility

MIP*

MIPfirst

Set of Solutions Influence the Users Learning

Set of Solutions Influence the Users Learning

IDEA: to artificially modify users' utility

AIM: all users prefer a solution thatlead to a feasible global solution

Explicit Approaches:intervention

(possible) future reward

Implicit Approaches:discountstaxation

Set of Solutions Influence the Users Learning

IDEA: to artificially modify users' utility

AIM: all users prefer a solution thatlead to a feasible global solution

Explicit Approaches:intervention

(possible) future reward

Implicit Approaches:discountstaxation

Set of Solutions Influence the Users Learning

IDEA: to artificially modify users' utility

AIM: all users prefer a solution thatlead to a feasible global solution

Explicit Approaches:intervention

(possible) future reward

Implicit Approaches:discountstaxation

Set of Solutions Influence the Users Learning

IDEA: to artificially modify users' utility

AIM: all users prefer a solution thatlead to a feasible global solution

Explicit Approaches:intervention

(possible) future reward

Implicit Approaches:discountstaxation

Set of Solutions Influence the Users Learning

IDEA: to artificially modify users' utility

AIM: all users prefer a solution thatlead to a feasible global solution

Explicit Approaches:intervention

(possible) future reward

Implicit Approaches:discountstaxation

Set of Solutions Influence the Users Learning

Set of Solutions Influence the Users Learning

1) utility model: what users like

2) response model: how users behave

3) belief update: done after each user's action

Set of Solutions Influence the Users Learning

MIPfirst

MIPothers

modifyusersutility

MIP*

standardlearning

techniques

PROBLEM:

properties guaranteed by the MIPs may not hold when taxation is applied

Set of Solutions Influence the Users Learning

MIPfirst

MIPothers

modifyusersutility

MIP*

standardlearning

techniques

MISSING:

active learning

PROBLEM:

properties guaranteed by the MIPs may not hold when taxation is applied

Set of Solutions Influence the Users Learning

MIPfirst

MIPothers

modifyusersutility

MIP*

standardlearning

techniques

Utility Model Response Model Belief Update

Noiseless

Constant Noise

Logit Noise

Model

Utility Model Response Model Belief Update

Noiseless

Constant Noise

Logit Noise

Model

Utility Model Response Model Belief Update

Noiseless

Constant Noise

Logit Noise

Model

Utility Model Response Model Belief Update

Noiseless

Constant Noise

Logit Noise

Model

Utility Model Response Model Belief Update

Noiseless

Constant Noise

Logit Noise

Model

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPs + Taxation

MIPfirst

MIPothers

MIP*

Sponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

ConstraintsSponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Sponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Logit Model

Sponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Logit Model

Sponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Logit Model

Sponsored Solution

MIPs + Taxation

MIPfirst

MIPothers

MIP*

MIPfirst

Objective

Constraints

Noiseless and Constant Noise Models

Logit Model

Sponsored Solution

MIPs + Taxation + Active Learning

Select the Set of Solutions such thatthe Expected Value Of Information (EVOI)

is maximized

MIPs + Taxation + Active Learning

Select the Set of Solutions such thatthe Expected Value Of Information (EVOI)

is maximized

MIPothers

MIPs + Taxation + Active Learning

Select the Set of Solutions such thatthe Expected Value Of Information (EVOI)

is maximized

MIPothers

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

max EVOI → max Expected Utility

MIPs + Taxation + Active Learning

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

Query

Set of OptionsRecommend an Option

MIPs + Taxation + Active Learning

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

Query

Set of OptionsRecommend an Option

In the Ridesharing scenario:

commend a set of options - only global solutions have an utility for the system

- any solution may have an utility for the system

MIPs + Taxation + Active Learning

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

Query

Set of OptionsRecommend an Option

In the Ridesharing scenario:● apply V. and B. work

commend a set of options - only global solutions have an utility for the system

- any solution may have an utility for the system

same results

the result holds also in this case

MIPs + Taxation + Active Learning

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

Query

Set of OptionsRecommend an Option

In the Ridesharing scenario:● apply V. and B. work● recommend a set of options - only global solutions have an utility for the system

- any solution may have an utility for the system

same results

the result holds also in this case

MIPs + Taxation + Active Learning

“Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set”Paolo Viappiani and Craig Boutilier (NIPS 2010)

Query

Set of OptionsRecommend an Option

In the Ridesharing scenario:● apply V. and B. work● recommend a set of options - only global solutions have an utility for the system

- any solution may have an utility for the system

same results

same results

the result holds also in this case

MIPs + Taxation + Active Learning

Conclusion

Ridesharing

as real world application

uncertainty due to human behaviour

imposing → recommend

recommendation set + influence users + learning

Thank you - Questions?

Conclusion

Ridesharing

as real world application

uncertainty due to human behaviour

imposing → recommending

recommendation set + influence users + learning

Thank you - Questions?

Ridesharing

as real world application

uncertainty due to human behaviour

imposing → recommending

recommendation set + influence users + learning

Ridesharing

as real world application

uncertainty due to human behaviour

imposing → recommending

recommendation set + influence users + learning