Sticky Content and the Structure of the Web Scott Duke Kominers - PowerPoint PPT Presentation

Introduction Attracting vs. Entrapping Recall our examples of sticky content: news/weather updates horoscopes webmail online games Definitions Attracting sticky content – attracts Entrapping sticky content Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 4 / 17

Introduction Attracting vs. Entrapping Recall our examples of sticky content: news/weather updates horoscopes webmail online games Definitions Attracting sticky content – attracts Entrapping sticky content – attracts AND entraps Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 4 / 17

Introduction Road Map We will... Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Based upon Katona and Sarvary (2009) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Based upon Katona and Sarvary (2009) Discuss effects of sticky content Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Based upon Katona and Sarvary (2009) Discuss effects of sticky content Attracting Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Based upon Katona and Sarvary (2009) Discuss effects of sticky content Attracting Entrapping Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

Introduction Road Map We will... Model sticky content Based upon Katona and Sarvary (2009) Discuss effects of sticky content Attracting Entrapping Conclude Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 5 / 17

The Model The Internet Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model The Internet Two parties of interest Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model The Internet Two parties of interest Content providers (“sites”) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model The Internet Two parties of interest Content providers (“sites”) Consumers Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model The Internet Two parties of interest Content providers (“sites”) – finitely many, n Consumers Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model The Internet Two parties of interest Content providers (“sites”) – finitely many, n Consumers – measure 1 Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 6 / 17

The Model Sites Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) sticky content parameter s i Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) sticky content parameter s i ...and links Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) sticky content parameter s i ...and links sold in a market Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) sticky content parameter s i ...and links sold in a market q i := per-click price of a link from site i Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Sites Parameters... commercial content parameter c i ∈ [0 , 1] (sale value) sticky content parameter s i ...and links sold in a market q i := per-click price of a link from site i ( ∂ q i ∂ c i > 0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 7 / 17

The Model Consumers Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers browse the web Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers browse the web Question How can we track consumer traffic? Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers browse the web Question How can we track consumer traffic? Answer PageRank! Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers browse the web Question How can we track consumer traffic? Answer PageRank! Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers randomly walk the web Question How can we track consumer traffic? Answer PageRank! Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers randomly walk the web Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers randomly walk the web, buying content from the sites they visit Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Consumers Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 8 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  1 i = j , d out +1  i  1 M ij = i → j , d out +1 i  0 i �→ j .  Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  1 i = j , d out +1  i  1 M ij = i → j , d out +1 i   0 i �→ j . r ( t +1) = δ · r ( t ) · M + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 9 / 17

The Model Attracting Sticky Content Remarks Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 10 / 17

The Model Attracting Sticky Content Remarks In the case s i ≡ s Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 10 / 17

The Model Attracting Sticky Content Remarks In the case s i ≡ s , r (0) = � 1 n , . . . , 1 � . n Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 10 / 17

The Model Attracting Sticky Content Remarks In the case s i ≡ s , r (0) = � 1 n , . . . , 1 � . n We recover the model of Katona and Sarvary ( Marketing Science , 2009). Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 10 / 17

Results Attracting Sticky Content Equilibrium Results Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Proposition Set of network equilibria is independent of sticky content distribution. Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Proposition Set of network equilibria is independent of sticky content distribution. Corollary In equilibrium, out-degree weakly decreases in c i . Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Proposition Set of network equilibria is independent of sticky content distribution. Corollary In equilibrium, out-degree weakly decreases in c i . Corollary In equilibrium, in-degree and limit traffic increase in c i . Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Corollary Attracting sticky content is strictly beneficial for sites. Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Corollary Attracting sticky content is strictly beneficial for sites. Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Corollary Attracting sticky content is strictly beneficial for sites. And now for something... Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

Results Attracting Sticky Content Equilibrium Results Corollary Attracting sticky content is strictly beneficial for sites. And now for something... ...surprisingly different. Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 11 / 17

The Model Entrapping Sticky Content Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17

The Model Entrapping Sticky Content Consumers (Attracting Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  1 i = j , d out +1  i  1 M ij = i → j , d out +1 i   0 i �→ j . r ( t +1) = δ · r ( t ) · M + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17

The Model Entrapping Sticky Content Consumers (Entrapping Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  1 i = j , d out +1  i  1 M ij = i → j , d out +1 i   0 i �→ j . r ( t +1) = δ · r ( t ) · M + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17

The Model Entrapping Sticky Content Consumers (Entrapping Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  s i i = j , d out + s i  i  1 M ij = i → j , d out + s i i   0 i �→ j . r ( t +1) = δ · r ( t ) · M + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17

The Model Entrapping Sticky Content Consumers (Entrapping Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M := ( M ij ) 1 ≤ i , j ≤ n , where  s i i = j , d out + s i  i  1 M ij = i → j , d out + s i i   0 i �→ j . r ( t +1) = δ · r ( t ) · M + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17

The Model Entrapping Sticky Content Consumers (Entrapping Sticky Content) Measure 1 of consumers randomly walk the web, buying content from the sites they visit with probability 1 Starting distribution depends on stickiness: � s 1 S , . . . , s n r (0) = � , S where S = � n i =1 s i . Transition matrix is M ′ := ( M ′ ij ) 1 ≤ i , j ≤ n , where  s i i = j , d out + s i  i  M ′ 1 ij = i → j , d out + s i i  0 i �→ j .  r ( t +1) = δ · r ( t ) · M ′ + (1 − δ ) · r (0) Scott Duke Kominers (Harvard) NetEcon’09 – July 7, 2009 12 / 17