# Advanced Topics in Search Theory - PowerPoint PPT Presentation

PPT – Advanced Topics in Search Theory PowerPoint presentation | free to view - id: 7afc87-NTBjM

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

## Advanced Topics in Search Theory

Description:

### Advanced Topics in Search Theory 1 - Introduction – PowerPoint PPT presentation

Number of Views:17
Avg rating:3.0/5.0
Slides: 52
Provided by: acil150
Category:
Tags:
Transcript and Presenter's Notes

Title: Advanced Topics in Search Theory

1
• 1 - Introduction

2
In Todays Class
• Course procedures
• What is economic search?
• Characteristics of economic search
• Classical models in Search Theory
• One Sided
• Two-Sided
• Mediated Search
• Reservation-Value based search

3
Goal
• Get familiar with the concept of economic
search
• Learn and master the main principles of economic
search
• One-sided
• Two-sided

4
Course Procedures
• Course web-site can be found here
• http//www.cs.biu.ac.il/sarned/Courses/search/
• Teacher David Sarne (sarned_at_cs.biu.ac.il)
• Office hours Thu 1500-1600 (building 216, room
2)
• Course exercises 20
• Course final exam 80

5
Course Plan
1 Introduction to Search Theory
2 Pandoras Problem
3 One-Sided Search principles and optimal strategy
4 One sided search with unknown distribution
5 Concurrent search
6 Cooperative Search
7 The secretary Problem
8 Market throughput in one-sided search
9 Two-Sided Search with no search costs
10 Two-Sided Search with search costs multi-type
11 Two-Sided Search with search costs with one and two types
12 Throughput in two-sided search
13 Two-sided search with mediators
6
Disclaimer
• Search in AI deals with finding nodes having
certain properties in a graph (find an optimal
path from the initial node to a goal node if one
exists)
• Branch and bound
• A
• Hill climbing
• This is not what we are interested in (at least
in this course)
• We deal with economic search

7
Have you searched for something lately?
• Can you give examples for what youve searcher
for?

8
Searching What?
• Everything!
• Searching for a partner
• Searching for a job
• Searching for a product
• Searching for a parking space
• Searching for a java class (reuse)
• Search for a thesis advisor

The goal here is to optimize the process rather
than ending up with the optimal search object
9
How about the secretary problem?(also known
as the marriage problem, the sultan's dowry
problem, the fussy suitor problem)
• There is a single secretarial position to fill.
• There are n applicants for the position, and the
value of n is known.
• The applicants can be ranked from best to worst
with no ties.
• The applicants are interviewed sequentially in a
random order, with each order being equally
likely.
• After each interview, the applicant is accepted
or rejected.
• The decision to accept or reject an applicant can
be based only on the relative ranks of the
applicants interviewed so far.
• Rejected applicants cannot be recalled.
• The object is to select the best applicant. The
payoff is 1 for the best applicant and zero
otherwise.

10
Example - Marriage Marketlegacy domain (search
pioneers)
f(x)

11
Statistics Reminder
• given a continuous random variable X, we denote
• The probability density function, pdf as f(x).
(also known as the probability distribution
function and the probability mass function)
• The cumulative distribution function, cdf, as
F(x).
• The pdf and cdf give a complete description of
the probability distribution of a random variable

12
PDF
• The pdf of X, is a function f(x) such that for
two numbers, a and b with ab
• That is, the probability that X takes on a value
in the interval a, b is the area under the
density function from a to b.

13
CDF
• Thecdf is a function F(x), defined for a number x
by
• That is, for a given value x, F(x) is the
probability that the observed value of X will be
at most x.

14
????? ??????? ?????
f(x)0.01
200
300
15
??????? ?????
• ????? f(x) ??? ?????? ?? P(x)
• ???? ????? ?????, P(2)1/6

16
Sampling from the distribution
f(t)
f4
P2
f3
P4
f2
P1
f1
P3
t
x1
x2
x3
x4
x5
x
• Draw a random value from a uniform distribution
• Take the value for which the CDF equals the value
drawn

17
Fitting a Distribution
• Visualize the Observed Data (decide on how to
divide date to bins)
• Come up with possible theoretical distributions
• Test goodness-of-fit and p-values based on the
empirical distribution function (EDF)
• Kolmogorov-Smirnov
• Chi-Square
• Anderson-Darling

measures of discrepancy between the empirical
distribution function and the cumulative
distribution function based on a specified
distribution
18
(No Transcript)
19
Comparison Shopping Agents (CSAs)
• Shopbots and Comparison Shopping
• automatically query multiple vendors for price
information
• Growing market, growing interest

comparison-shopping agents
20
Comparison Shopping Agents (CSAs)
Offline - central DB of prices (daily updated)
Real-time querying upon receiving a request
21
Real-Time Querying (CSAs)
• Ever-increasing frequency of price updates
• Dynamic pricing theories (based on competitors
prices) Greenwald and Kephart, 1999
• Hit and run sales strategies (short term price
promotions at unpredictable intervals) Baye et
al, 2004

Assumption Future CSAs will use real-time
(costly) querying
22
Exercise
• Select 5 different products (preferable
electronics, computers etc.)
• Collect Prices for these products over the
internet build their empirical distribution (at
least 50 prices for each)
• Fit to a know distribution or describe the
empirical distribution obtained
• Calculate the optimal search rule
• Send all the data with your file

23
Example - Marriage Marketlegacy domain (search
pioneers)
f(x)

Should I try to do better?
24
Can we do better?
• Yes we can!
• However, it has a cost
• Thus a search strategy is needed

Strategy (opportunities, time,
cost)-gt(terminate, resume)
25
Search Characteristics
• A distribution of plausible opportunities
• The searcher is interested in exploiting one
opportunity
• Unknown value of specific opportunities
• Search costs

26
Searching What?
Application Cost Opportunity
Marriage Market Time / money / loneliness Better partner
Job Market Time / money / confidence Better job
Product Time / money Better price / performance
Parking time Closer parking space
Looking for a thesis advisor Working with him a little More interesting thesis

Anyone searched for an apartment in her life?
What made you take the one you are living in?
Anyone sold an apartment in her life? What made
you accept the winning bid?
The key concept dont attempt to find the best
opportunity, instead find the best policy
27
The search strategy
• After each draw, the searcher has a choice
• Keep what he has
• Draw another opportunity from the distribution
F(), at a cost c
• Notice the net profit is a random variable whose
value depends both on the actual draws and on his
decisions to accept or reject particular
opportunities

28
The Goal
• Maximize the expected value of the net profit

Application Cost Opportunity
Marriage Market Time / money / loneliness Better partner
Job Market Time / money / confidence Better job
Product Time / money Better price / performance
Parking time Closer parking space
29
The optimal strategy
• Let V be the expected profit if following the
optimal strategy
• Clearly the searcher should never accept an
opportunity with a value less than V
• If he rejects the opportunity, he is in the same
situation as a searcher who is starting anew
expect profit V
• Therefore

30
Example - Marriage Market
f(x)
Reservation Value - x

Should I try to do better?
In a simple infinite horizon model - doesnt
depend on history
31
What is a reservation value?
• Its a threshold for decision making!
• Example Krovim Krovim
• The reservation property of the optimal search
rule is a consequence of the stationarity of the
search problem (a searcher discarding an
opportunity is in exactly the same position as
before starting the search)

32
Example - Marriage Market
f(x)
Terminate Search
Resume Search - sample one more
Reservation Value - x

Should I try to do better?
In a simple infinite horizon model - doesnt
depend on history
33
The optimal Reservation Value
f(x)
Terminate Search
Resume Search - sample one more

x
Distribution of utilities in the environment
(p.d.f / c.d.f)
Expected utility when using reservation value x
Search cost
34
The Reservation Value Concept
Distribution of utilities in the environment
(p.d.f / c.d.f)
Expected utility when using reservation value x
Search cost
What is x that maximizes V(x)?
35
The Reservation Value Concept
36
Example - Marriage Market
f(x)
Terminate Search
Resume Search - sample one more
Reservation Value - x

Should I try to do better?
The expected utility from accepting only better
partner than the optimal reservation value woman
will yield an expected overall utility equal to
the lowest partner Im willing to accept
37
Some more interesting interpretations
38
Some more interesting interpretations (2)
Stop searching and keeping x
Searching exactly one more time
39
Myopic rule
• Important property of the optimal search rule
myopic
• The searcher will never decide to accept an
opportunity he has rejected beforehand
• Searcher cares only about whether or not he wants
the opportunity now
• Therefore, we dont care for the recall option

40
Also notice that
• and

Bernoulli trial is an experiment whose outcome is
random and can be either of two possible
outcomes, "success" and "failure".
41
Calculating the optimal RV
Notice that
42
Calculating the optimal RV
Therefore
43
CS economic search domains
• CSAs
• Job scheduling
• Searching for media in P2P
• Classical tradeoff time it takes to process vs.
time it takes to find a strong processor

44
The Scheduling Problem
Processor 1
Price quote (q)
c1
Processor 2
c2
Price quote (q)
Scheduling Process
Proxy
cN
Processor N
Price quote (q)
45
WorkFlow
• Contact proxy to learn about available processors
• Query processors by using the proxy
• Each query delays you in c_i seconds
• Each query will return the temporary load on the
server (this value will not change as long as
current job is not scheduled)
• Keep on querying until you are ready to schedule

46
The Goal is
• To schedule the job in a way that minimizes the
EXPECTED overall delay
• Overall delay all delays due to queries the
time job waits in queue of the selected processor

47
Problem 1
• You are about to purchase an iPod touch over the
internet
• You estimate the price distribution of the
product over the different sellers to be uniform
between 200-300 dollars
• You can search by yourself, by visiting different
web-sites the cost of time for obtaining a
price quote is 1
• How will you search? What will be your expected
cost? Whats the mean of the number of merchants
youll visit?

48
Solution
f(x)
0.01
200
300
• Sequential search

49
Find the minimum cost
50
Verification
• V(x)x?
• Mean number of merchants visited
• Mean payment to merchant 214.14-7.14207
• (notice its less than minimum of sampling 7
merchants)

V
51
Alternative Solution
f(x)
0.01
200
300
• Sequential search

marginal benefit
cost of search
x