Showing posts with label trust. Show all posts
Showing posts with label trust. Show all posts
Sunday, December 9, 2007
Lightweight Distributed Trust Propagation
I just finished to present our work at ICDM. Here are the slides (also in ppt).
Trust Bootstrapping: TRULLO @ Mobiquitous
At Mobiquitous 2007, we presented TRULLO. Here are the slides (some animations do not work properly in slideshare, sorry). A brief description follows.
Situation: Using mobile devices, such as smart phones, people may create and distribute different types of digital content (e.g., photos, videos). One of the problems is that digital content, being easy to create and replicate, may likely swamp users rather than informing them. To avoid that, users may run trust models on their mobile devices. A trust model is a piece of software that keeps track of who provides quality content and who does not.
Problem: Devices should be able to set their initial trust for other devices. One way of doing so is for devices to learn from their own past experiences. To see how, consider the following quotes about human trust: ``We may initially trust or not trust those involved on our projects based on past experience'', and ``If your boyfriend is unfaithful, you won't initially trust the next man you date'' :-) Algorithms that model human trust on pervasive devices, one might say, ought to do the same thing - they should assign their initial trust upon `similar' past experiences.
Existing Solutions: Existing solutions usually require an ontology upon which they decide which past experiences are similar, and, in so doing, they require both that the same ontology is shared by all users (which is hardly the case in reality) and that users agree on that ontology for good (ie, the ontology is not supposed to change over time) :-(
Proposal: TRULLO gathers ratings of past experiences in a matrix, learns staticial "features" from that matrix, and combines those features to set initial trust values. It works quite well in a simulated antique market and its implementation is reasonably fast on a Nokia mobile phone.
Future: TRULLO does not work if one does not have past experiences. That is why we will propose a distributed trust propadation algorithm (pdf)
Situation: Using mobile devices, such as smart phones, people may create and distribute different types of digital content (e.g., photos, videos). One of the problems is that digital content, being easy to create and replicate, may likely swamp users rather than informing them. To avoid that, users may run trust models on their mobile devices. A trust model is a piece of software that keeps track of who provides quality content and who does not.
Problem: Devices should be able to set their initial trust for other devices. One way of doing so is for devices to learn from their own past experiences. To see how, consider the following quotes about human trust: ``We may initially trust or not trust those involved on our projects based on past experience'', and ``If your boyfriend is unfaithful, you won't initially trust the next man you date'' :-) Algorithms that model human trust on pervasive devices, one might say, ought to do the same thing - they should assign their initial trust upon `similar' past experiences.
Existing Solutions: Existing solutions usually require an ontology upon which they decide which past experiences are similar, and, in so doing, they require both that the same ontology is shared by all users (which is hardly the case in reality) and that users agree on that ontology for good (ie, the ontology is not supposed to change over time) :-(
Proposal: TRULLO gathers ratings of past experiences in a matrix, learns staticial "features" from that matrix, and combines those features to set initial trust values. It works quite well in a simulated antique market and its implementation is reasonably fast on a Nokia mobile phone.
Future: TRULLO does not work if one does not have past experiences. That is why we will propose a distributed trust propadation algorithm (pdf)
Sunday, November 25, 2007
Cold Reading, Statistical Discrimination and Initial Trust
"Cold reading is a technique used to convince another person that the reader knows much more about a subject than they actually do. Even without prior knowledge of a person, a practiced cold reader can still quickly obtain a great deal of information about the subject by carefully analyzing the person's body language, clothing or fashion, hairstyle, gender, sexual orientation, religion, race orethnicity, level of education, manner of speech, place of origin, etc. This technique is also called offender profiling. Cold readers commonly employ high probability guesses about the subject, quickly picking up on signals from their subjects as to whether their guesses are in the right direction or not, and then emphasizing and reinforcing any chance connections the subjects acknowledge while quickly moving on from missed guesses".
This definition of cold reading reminded me of Posner's (harsh) review of Blink - Blinkered (html). There are two points from Posner's essay that may suggest how person A may set her initial trust in B. The first point may suggest that A does so based not only on B's behavior but also on the (social) group(s) to which B belongs. The second point may remind us that: asking for recomendations about B may be costly; and that Bayesian reasoning may help in rationally deciding whether to trust. Here are the two (by now-coveted) points:
(1) "If two groups happen to differ on average, even though there is considerable overlap between the groups, it may be sensible to ascribe the group's average characteristics to each member of the group, even though one knows that many members deviate from the average. An individual's characteristics may be difficult to determine in a brief encounter, and a salesman cannot afford to waste his time in a protracted one, and so he may quote a high price to every black shopper even though he knows that some blacks are just as shrewd and experienced car shoppers as the average white, or more so. Economists use the term "statistical discrimination" to describe this behavior. It is a better label than stereotyping for what is going on in the auto-dealer case, because it is more precise and lacks the distracting negative connotation of stereotype, defined by Gladwell as "a rigid and unyielding system." But is it? Think of how stereotypes of professional women, Asians, and homosexuals have changed in recent years. Statistical discrimination erodes as the average characteristics of different groups converge."
(2) " Such pratfalls, together with the inaptness of the stories that constitute the entirety of the book, make me wonder how far Gladwell has actually delved into the literatures that bear on his subject, which is not a new one. These include a philosophical literature illustrated by the work of Michael Polanyi on tacit knowledge and on "know how" versus "know that"; a psychological literature on cognitive capabilities and distortions; a literature in both philosophy and psychology that explores the cognitive role of the emotions; a literature in evolutionary biology that relates some of these distortions to conditions in the "ancestral environment" (the environment in which the human brain reached approximately its current level of development); a psychiatric litetature on autism and other cognitive disturbances; an economic literature on the costs of acquiring and absorbing information; a literature at the intersection of philosophy, statistics, and economics that explores the rationality of basing decisions on subjective estimates of probability (Bayes's Theorem); and a literature in neuroscience that relates cognitive and emotional states to specific parts of and neuronal activities in the brain. "
This definition of cold reading reminded me of Posner's (harsh) review of Blink - Blinkered (html). There are two points from Posner's essay that may suggest how person A may set her initial trust in B. The first point may suggest that A does so based not only on B's behavior but also on the (social) group(s) to which B belongs. The second point may remind us that: asking for recomendations about B may be costly; and that Bayesian reasoning may help in rationally deciding whether to trust. Here are the two (by now-coveted) points:
(1) "If two groups happen to differ on average, even though there is considerable overlap between the groups, it may be sensible to ascribe the group's average characteristics to each member of the group, even though one knows that many members deviate from the average. An individual's characteristics may be difficult to determine in a brief encounter, and a salesman cannot afford to waste his time in a protracted one, and so he may quote a high price to every black shopper even though he knows that some blacks are just as shrewd and experienced car shoppers as the average white, or more so. Economists use the term "statistical discrimination" to describe this behavior. It is a better label than stereotyping for what is going on in the auto-dealer case, because it is more precise and lacks the distracting negative connotation of stereotype, defined by Gladwell as "a rigid and unyielding system." But is it? Think of how stereotypes of professional women, Asians, and homosexuals have changed in recent years. Statistical discrimination erodes as the average characteristics of different groups converge."
(2) " Such pratfalls, together with the inaptness of the stories that constitute the entirety of the book, make me wonder how far Gladwell has actually delved into the literatures that bear on his subject, which is not a new one. These include a philosophical literature illustrated by the work of Michael Polanyi on tacit knowledge and on "know how" versus "know that"; a psychological literature on cognitive capabilities and distortions; a literature in both philosophy and psychology that explores the cognitive role of the emotions; a literature in evolutionary biology that relates some of these distortions to conditions in the "ancestral environment" (the environment in which the human brain reached approximately its current level of development); a psychiatric litetature on autism and other cognitive disturbances; an economic literature on the costs of acquiring and absorbing information; a literature at the intersection of philosophy, statistics, and economics that explores the rationality of basing decisions on subjective estimates of probability (Bayes's Theorem); and a literature in neuroscience that relates cognitive and emotional states to specific parts of and neuronal activities in the brain. "
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