learning to trust on the move TIME: workshop on trust in mobile environments neal lathia

(about us) neal lathia (2nd yr phd): stephen hailes & licia capra department of computer science university college london

why do we need trust?

trust enables interaction in environments that are characterised by uncertainty

a mobile world: how many dogs?

… but what is trust?

“ trust (or, symmetrically, distrust) is a particular level of the subjective probability with which an agent will perform a particular action, both before [we] can monitor such action (or independently of his capacity of ever to be able to monitor it) and in a context in which it affects [our] own action.” [Gambetta]

“ trust (or, symmetrically, distrust) is a particular level of the subjective probability with which an agent will perform a particular action, both before [we] can monitor such action (or independently of his capacity of ever to be able to monitor it) and in a context in which it affects [our] own action.” [Gambetta]

“ trust (or, symmetrically, distrust) is a particular level of the subjective probability with which an agent will perform a particular action, both before [we] can monitor such action (or independently of his capacity of ever to be able to monitor it) and in a context in which it affects [our] own action.” [Gambetta] four characteristics of trust emerge

1. predictability

2. learning and confidence

3. knowledge propagation

4. behavioural enforcement and incentives

4. behavioural enforcement and incentives

… is this how we trust each other?

1. predictability…? 2. learning and confidence…? 3. propagation…? 4. incentives…?

=?

does this matter?

mobile trust = machine learning problem (semi-supervised, …mechanism design)

machine learning problem is limited by how much we can learn: quantity and how well we can learn from it: quality

machine learning problem is limited by how much we can learn: quantity and how well we can learn from it: quality

quality: (design) 1. system objectives 2. resource availability

quality: (design) 1. system objectives 2. resource availability (context) 3. behavioural homogeneity 4. mobility

learning in the reality mining dataset quantity: mobility – learning strategy OM: only me NR: neighbour recommendations RS: reputation server

learning in the reality mining dataset ~250 i.d.s OM: only me NR: neighbour recommendations RS: reputation server peer A peer B

peer A peer B

peer A peer B how much we can learn from is affected by how we learn

learning in the reality mining dataset quantity: cooperation binary model: fully cooperate never cooperate

there is still a lot to gain when majority do not cooperate

machine learning problem is limited by how much we can learn: quantity and how well we can learn from it: quality

machine learning problem is limited by how much we can learn: quantity and how well we can learn from it: quality “ I trust the farmer to feed me every day!”

machine learning problem is limited by how much we can learn: quantity and how well we can learn from it: quality

learning to trust on the move TIME: workshop on trust in mobile environments n.lathia@cs.ucl.ac.uk neal lathia http://mobblog.cs.ucl.ac.uk some images: flickr creative commons