Download - Airlines Model
![Page 1: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/1.jpg)
A Model On
DOMESTIC AIRLINE INDUSTRY IN INDIA
Using Graph Theory and Game Theory
©Rupakshi Bhatia
![Page 2: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/2.jpg)
FOCAL AIRLINE:
©Rupakshi Bhatia
![Page 3: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/3.jpg)
The Indigo Destinations Map
[8].
©Rupakshi Bhatia
![Page 4: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/4.jpg)
THE PROBLEMS
1. What are the least number of “hops” that one would have to make to travel from one city to another?
2. How do we choose the flight that suits us the best?
3. What determines the pricing of Airline tickets?
©Rupakshi Bhatia
![Page 5: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/5.jpg)
WHY GRAPH THEORY AND GAME THEORY?
• Graph Theory allows us to pictorially represent the relationship between airline routes and cities, and thus helps us to deal with Problems 1 and 2.
• Game Theory allows us to study the behavior of businessmen handling firms and their reasons behind the pricing of airline tickets, thus, helping us to address Problem 3.
©Rupakshi Bhatia
![Page 6: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/6.jpg)
THE GRAPH
The graph is formally given in the form G = (V(G), E(G), ∑, lab)where
• V(G) is a finite set of vertices; |V(G)|=28 • E(G) ⊆ V2 is a set of undirected edges• ∑ is a finite nonempty set of vertex labels, and • lab: V(G)∑ is a labelling function where for v ϵ V(G), lab(v) is the label of vertex v [1].
Remarks:1. Graph G is not a Complete Graph.2. Graph G is a Connected Graph.
©Rupakshi Bhatia
![Page 7: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/7.jpg)
v lab(v)
Agartala v1
Ahmedabad v2
Bangalore v3
Bhubaneswar v4
Chandigarh v5
Chennai v6
Coimbatore v7
Delhi v8
Dibrugarh v9
Goa v10
Guwahati v11
Hyderabad v12
Imphal v13
Indore v14
Jammu v16
Kochi v17
Kolkata v18
Lucknow v19
Mumbai v20
Nagpur v21
Patna v22
Pune v23
Raipur v24
Srinagar v25
Trivandrum v26
Vadodara v27
Vishakhapatnam v28
v lab(v)
Jaipur v15
©Rupakshi Bhatia
![Page 8: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/8.jpg)
The Adjacency Matrix
• 28 x 28 symmetric binary matrix A = [aij] such that:
aij = 1, if {vi, vj} ϵ E(G);= 0, otherwise
©Rupakshi Bhatia
![Page 9: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/9.jpg)
The Adjacency Matrixaga ahm ben bhu cha che coi del dib goa guw hyd imp ind jai jam koc kol luc mum nag pat pun rai sri tri vad vis
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 v23 v24 v25 v26 v27 v28
aga v1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
ahm v2 0 0 1 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0
ben v3 0 1 0 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 0 0
bhu v4 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1
cha v5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
che v6 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1
coi v7 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
del v8 0 1 1 1 0 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0
dib v9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
goa v10 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0
guw v11 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
hyd v12 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1
imp v13 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
ind v14 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0
jai v15 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
jam v16 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
koc v17 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0
kol v18 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1
luc v19 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0
mum v20 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 0
nag v21 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0
pat v22 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
pun v23 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
rai v24 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
sri v25 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
tri v26 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
vad v27 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
vis v28 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
©Rupakshi Bhatia
![Page 10: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/10.jpg)
• A Flight(F) is an open path between any two vertices (cities) of the graph.
It is a finite sequence of the form vi0, ej1, vi1, ej2, …, ejk, vik, which consists of alternating vertices and edges of G, where vix ≠ viy ∀ x≠y and ejx ≠ ejy ∀ x≠y.
vi0 is the initial city (vertex) and vik is the terminal city (vertex).
k is the length of the flight. A flight cannot be a zero-length path.
• Defining, two cities vx and vy are CONNECTED if there is a DIRECT FLIGHT between them.
©Rupakshi Bhatia
![Page 11: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/11.jpg)
• 28 x 28 symmetric matrix C = [cij] such that:
cij = kij, if {vi, vj} ϵ E(G);= 0, otherwise
Here, kij is the number of edges joining vi and vj.
The Connection Matrix
©Rupakshi Bhatia
![Page 12: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/12.jpg)
The Connection Matrixaga ahm ben bhu cha che coi del dib goa guw hyd imp ind jai jam koc kol luc mum nag pat pun rai sri tri vad vis
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 v23 v24 v25 v26 v27 v28
aga v1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
ahm v2 0 0 1 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0
ben v3 0 1 0 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 0 0
bhu v4 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1
cha v5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
che v6 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1
coi v7 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
del v8 0 1 1 1 0 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0
dib v9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
goa v10 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0
guw v11 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
hyd v12 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 0 0 0 1
imp v13 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
ind v14 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0
jai v15 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
jam v16 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
koc v17 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0
kol v18 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1
luc v19 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0
mum v20 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 0
nag v21 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0
pat v22 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
pun v23 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
rai v24 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
sri v25 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
tri v26 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
vad v27 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
vis v28 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
©Rupakshi Bhatia
![Page 13: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/13.jpg)
• The Connection Matrix of G is the same as the Adjacency Matrix of G since the maximum number of edges between any two vertices of G is 1.
• Out of a total of 28C2=378 pairs of vertices, about 6 cases existed where vx was connected to vy but vy was not connected to vx (vx, vy ϵ V(G) ) . Since 6/378=0.015873 is a very small probability, we ignore such possibilities and assume that CONNECTED(vx, vy)=T ⇔ CONNECTED (vy, vx)=T.
Thus,• If there is a Direct Flight from vx to vy, then there is a Direct Flight from vy to vx as well.• The Adjacency and Connection Matrices are symmetric.
©Rupakshi Bhatia
![Page 14: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/14.jpg)
The Degree Matrix of G
• 28 x 28 diagonal matrix D = [dij] such that:
dij = deg(vi), if i=j;= 0, if i ≠ j.
• Degree of a vertex v, deg(v) is the number of edges incident with the vertex v, with loops counted twice.
©Rupakshi Bhatia
![Page 15: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/15.jpg)
The Degree Matrix of Gaga ahm ben bhu cha che coi del dib goa guw hyd imp ind jai jam koc kol luc mum nag pat pun rai sri tri vad vis
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 v23 v24 v25 v26 v27 v28
aga v1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ahm v2 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ben v3 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bhu v4 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cha v5 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
che v6 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
coi v7 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
del v8 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dib v9 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
goa v10 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
guw v11 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
hyd v12 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imp v13 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ind v14 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
jai v15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0
jam v16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
koc v17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0
kol v18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0
luc v19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0
mum v20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0
nag v21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0
pat v22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0
pun v23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0
rai v24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0
sri v25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0
tri v26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0
vad v27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
vis v28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
©Rupakshi Bhatia
![Page 16: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/16.jpg)
• 28 x 28 matrix P(G) = [pij] such that:
pij = 0, if i=j;= x otherwise.
Here, x is the length of the shortest path between vi and vj.
The Path Matrix of G
©Rupakshi Bhatia
![Page 17: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/17.jpg)
The Path Matrix of Gaga ahm ben bhu cha che coi del dib goa guw hyd imp ind jai jam koc kol luc mum nag pat pun rai sri tri vad vis
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 v23 v24 v25 v26 v27 v28
aga v1 0 2 2 2 3 2 3 2 2 3 1 2 1 3 2 3 3 1 3 2 2 2 3 2 3 3 3 2
ahm v2 2 0 1 2 2 1 2 1 2 1 2 1 2 2 1 2 2 1 2 1 2 2 1 2 2 2 2 2
ben v3 2 1 0 2 1 1 1 1 2 1 1 1 2 2 1 2 2 1 1 1 1 2 1 2 2 1 2 2
bhu v4 2 2 2 0 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1
cha v5 3 2 1 2 0 2 2 2 3 2 2 2 3 2 2 3 2 2 2 1 2 3 2 3 2 2 2 3
che v6 2 1 1 2 2 0 1 1 2 2 2 1 2 2 2 2 2 1 2 1 2 2 1 2 2 1 2 1
coi v7 3 2 1 2 2 1 0 1 3 2 2 1 3 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2
del v8 2 1 1 1 2 1 1 0 2 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2
dib v9 2 2 2 2 3 2 3 2 0 3 2 2 2 3 2 3 3 1 3 2 2 2 3 2 3 3 3 2
goa v10 3 1 1 2 2 2 2 1 3 0 2 2 3 1 2 2 2 2 2 1 2 2 2 2 2 2 2 3
guw v11 1 2 1 2 2 2 2 1 2 2 0 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2
hyd v12 2 1 1 1 2 1 1 1 2 2 2 0 2 2 1 2 1 1 1 1 1 2 1 1 2 2 2 1
imp v13 1 2 2 2 3 2 3 2 2 3 1 2 0 3 2 3 3 1 3 2 2 2 3 2 3 3 3 2
ind v14 3 2 2 2 2 2 2 1 3 1 2 2 3 0 2 2 2 2 2 1 1 2 2 1 2 2 2 3
jai v15 2 1 1 2 2 2 2 2 2 2 2 1 2 2 0 3 2 1 2 1 2 2 2 2 2 2 2 2
jam v16 3 2 2 2 3 2 2 1 3 2 2 2 3 2 3 0 2 2 2 2 2 2 2 2 2 3 2 3
koc v17 3 2 2 2 2 2 2 1 3 2 2 1 3 2 2 2 0 2 1 1 2 2 2 2 2 1 2 2
kol v18 1 1 1 1 2 1 2 1 1 2 1 1 1 2 1 2 2 0 2 1 1 1 2 1 2 2 2 1
luc v19 3 2 1 2 2 2 2 1 3 2 2 1 3 2 2 2 1 2 0 1 2 1 2 2 2 2 2 2
mum v20 2 1 1 1 1 1 1 1 2 1 2 1 2 1 1 2 1 1 1 0 1 2 2 2 1 1 1 2
nag v21 2 2 1 2 2 2 2 1 2 2 2 1 2 1 2 2 2 1 2 1 0 2 1 2 2 2 2 2
pat v22 2 2 2 2 3 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 0 2 2 2 3 2 2
pun v23 3 1 1 2 2 1 2 1 3 2 2 1 3 2 2 2 2 2 2 2 1 2 0 2 2 2 2 2
rai v24 2 2 2 2 3 2 2 1 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 0 2 3 2 2
sri v25 3 2 2 2 2 2 1 1 3 2 2 2 3 2 2 2 2 2 2 1 2 2 2 2 0 3 2 3
tri v26 3 2 1 2 2 1 2 2 3 2 2 2 3 2 2 3 1 2 2 1 2 3 2 3 3 0 2 2
vad v27 3 2 2 2 2 2 2 1 3 2 2 2 3 2 2 2 2 2 2 1 2 2 2 2 2 2 0 3
vis v28 2 2 2 1 3 1 2 2 2 3 2 1 2 3 2 3 2 1 2 2 2 2 2 2 3 2 3 0
©Rupakshi Bhatia
![Page 18: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/18.jpg)
• Service Frequency(f) :f: V x V ℕf(vx, vy) := Number of Flights from vx to vy per day
Assume that: • f(vx, vy) is constant throughout the week.• Aircrafts are large enough to satisfy expected demands; if not, frequency is increased to meet excess demands. • People value (i) Time(ii) Money(iii) Comfort
• Busiest Airport(s) or HUB(S) : Airport vertex with maximum degree.As can be seen from the degree matrix, the Busiest Airport is that of Delhi with degree 20.
©Rupakshi Bhatia
![Page 19: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/19.jpg)
Value of a Flight
Utility Function:X= {Time, Money, Comfort} is a set of parameters.U: X ℝ+ is a consumer-specific Utility Function representing the Preference Relation R ϶aRb ⇒ a » b and∀ a, b ϵ X , a » b ⇔ U(a) ≥ U(b)
whereR ⊂ X x X [5].
• a » a is true ∀ a ϵ X ⇒ R is Reflexive.• a » b ⇒ b » a. ∴ R is not Symmetric.• ∀a, b ∈ X, a » b ∧ b » a ⇒ a = b is not true. ∴ R is not Antisymmetric [4].• a » b ∧ b » c ⇒ a » c is true. ∴ R is Transitive.
©Rupakshi Bhatia
![Page 20: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/20.jpg)
Then, Value of a Flight, Val(F) = U(Time).Q(Time) + U(Money).Q(Money) + U(Comfort).Q(Comfort)whereQ(Time)=Quantity of Time savedQ(Money)=Quantity of Money savedQ(Comfort)=Quantity of Extra Comfort received.
Then, a customer will choose the Flight with the greatest Value.
For example, suppose•‘n’ choices of Flights {F1, F2, … Fn}• Take {t1, t2, …, tn} hours from the initial city to the customer’s destination,• Price {P1, P2, …, Pn} • Comfort level {C1, C2, …, Cn}.
Let t = max(t1, t2, …, tn) , P = max(P1, P2, …, Pn) , C=min(C1, C2, …, Cn).Then, Val(Fi)= [ U(Time).(t-ti) + U(Money).(P-Pi) + U(Comfort).(Ci-C) ]
©Rupakshi Bhatia
![Page 21: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/21.jpg)
If a journey involves taking multiple flights, Value of a journey = (Sum of values of all flights involved/Number of flights involved)-[U(Time). (Waiting time) ]
-c
The journey with the greatest value is then chosen.
©Rupakshi Bhatia
![Page 22: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/22.jpg)
DOMESTIC AIRLINE INDUSTRY: AN OLIGOPOLY
What is an oligopoly?It is a market structure in which only few sellers, each having a large market share offer similar or identical products [2].
‘Highly concentrated Markets’: Where most of the total market share is locked up by a small number of firms [3].Thus, an oligopoly is a highly concentrated market.
• One commonly used method of market concentration is the Herfindahl-Hirschman Index (HHI), given by
HHI = ∑Si2
i=1
N
whereSi denotes the percent of market controlled by the ith firmN denotes the number of firms in the market
©Rupakshi Bhatia
![Page 23: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/23.jpg)
Then,HHI > 1,800 denotes a highly concentrated market [6].
We have
[7].
*As in July 2012©Rupakshi Bhatia
![Page 24: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/24.jpg)
Thus, HHI for the Domestic Airline Industry in India = (20.7)2 + (18.3)2 + (6.9)2 + (27.3)2 + (19.5)2 + (7.4)2
= 1,991.29 > 1,800.
Therefore, Airline Industry is an Oligopoly.
©Rupakshi Bhatia
![Page 25: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/25.jpg)
PRICING STRATEGIESPrice Discrimination
Price Discrimination: The practice of selling the same good/service at different prices to different customers [2].Here, the service is a Flight ticket from vx to vy.
Indigo practices Price Discrimination in 3 ways:
1. One Way Ticket/ Round Trip TicketsPrice(Round Trip) < Price(One Way).
2. Age0, if 0 ≤ Age < 2
Let Ai= 1, if 2 ≤ Age < 122, if Age ≥ 12
for the ith customer.Then, A1 < A2 ⇒ Price(A1) < Price(A2).
{ ©Rupakshi Bhatia
![Page 26: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/26.jpg)
3. TimePrice(t) is an increasing function wheret ϵ [-365, 0)
Then,t1 < t2 ⇒ Price(t1) ≤ Price(t2) [8].
©Rupakshi Bhatia
![Page 27: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/27.jpg)
Does Indigo have an incentive to change price unilaterally?
• All other factors kept constant.• Set of Player, S = {A, B} where Player A: Indigo, Player B: Combined others• Strategy Set for A, SA = {Raise, Lower, Do Nothing}• Strategy Set for B, SB = {React, Don’t React}• Single-shot game• Game is not symmetric since SA ≠ SB
• Pay-offs represent monetary gains.
React Don’t React
Raise (20, 20) (-10, 40)
Lower (-5, -5) (80, -100)
Do Nothing (0, 0) (0, 0)
A
B
©Rupakshi Bhatia
![Page 28: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/28.jpg)
• What A would want: If A raises its price, B should reactIf A lowers its price, B should not react.
• What would actually happen(since the market is an Oligopoly): If A raises its price, B does not reactIf A lowers its price, B reacts.
Why?
•∏B(Raise, Don’t React)=40 ≥ ∏B(Raise, React)=20∴∏B(Raise, Don’t React) ≥ ∏B(Raise, sB) ∀ sB ϵ SB
• ∏B(Lower, React)=(-5) ≥ ∏B(Lower, Don’t React)=(-100)∴∏B(Lower, React) ≥ ∏B(Lower, sB) ∀ sB ϵ SB
Where ∏j(s, s’) is the payoff for player j when PA plays strategy s and PB
plays strategy s’ in a 2-player game.
©Rupakshi Bhatia
![Page 29: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/29.jpg)
Thus, The pay-off matrix for player A reduces to:
Raise -10
Lower -5
Do Nothing 0
Clearly,∏A(Do Nothing)=0 ≥ ∏A(s); s ϵ SB \ {Do Nothing}
∴ There is no incentive for A to change its price unilaterally, other factors remaining constant. So, a change in price is due to some other factors/conditions only.
A
©Rupakshi Bhatia
![Page 30: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/30.jpg)
Lowering and Raising of prices
Now, suppose A is incurring heavy monetary losses due to erosion of customer base.∴ A tries to reduce its losses by increasing its market share.Then,
React Don’t React
Raise 20 -10
Lower -5 80
Do Nothing -50 -50
A
B
©Rupakshi Bhatia
![Page 31: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/31.jpg)
Given that:B reacts when A lowers priceB does not react when A raises price.
∴ Pay-off matrix of A reduces to
Raise -10
Lower -5
Do Nothing -50
Clearly,∏A(Lower)=(-5) ≥ ∏A(s); s ϵ SB \ {Lower}
Thus, A lowers its price B reacts by lowering Price War Airline prices crash.
A
©Rupakshi Bhatia
![Page 32: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/32.jpg)
• Prices crash till they reach the ‘Crash Point’ where Revenue < Cost of production(service).
• Now, the game changes with changing pure strategy sets;SA = SB = {Compete, Collude}
Compete Collude
Compete (-5,-5) (0,0)
Collude (0,0) (20,20)
Claim: (Collude, Collude) is the Nash Equilibrium.
A
B
©Rupakshi Bhatia
![Page 33: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/33.jpg)
Proof:•∏A(Collude, Collude)=20 ≥ ∏A(Compete, Collude)=0∴∏A(Collude, Collude) ≥ ∏A(sA , Collude) ∀ sA ϵ SA
• ∏B(Collude, Collude)=20 ≥ ∏B(Collude, Compete)=0∴∏B(Collude, Collude) ≥ ∏B(Collude, sB) ∀ sB ϵ SB
• Thus, firms collude prices rise
• Prices rise till they reach the ‘Tipping Point’where Revenue < Cost of production(service).
• This cycle of price rise and crash continues.
Note that: Crash Point ≠ Tipping Point.Crash Point is reached when Revenue decreases beyond Cost of production due to Extremely Low Price.Tipping Point is reached when Revenue decreases beyond Cost of production due to Extremely High Price and Extremely Low Demand.
©Rupakshi Bhatia
![Page 34: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/34.jpg)
The Cycle of Price Changes
Crash Point
Tipping Point
Prices FallPrices RiseTipping Point
Crash Point
Price
TimeO
©Rupakshi Bhatia
![Page 35: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/35.jpg)
CONCLUSIONS
• The least lengths of paths that one would have to travel from one city to another vary from 1 to 3 which can be found using the Path Matrix.
• Consumers choose the flight with the greatest value, according to their Utilities of Time, Money and Comfort.
• A cycle of price changes continues in the Airline Industry with time, where the prices fall till a Crash Point, after which they start rising and rise till a Tipping Point, and then start falling again.
• At a given point in time, Indigo practices three kinds of Price Discrimination wherein the price of the tickets is determined by (i) Whether a person buys One-Way ticket or tickets for a Round Trip,(ii) The age group of the person: Infant, Child or Adult, and(iii) The time at which the ticket is being purchased.
©Rupakshi Bhatia
![Page 36: Airlines Model](https://reader034.vdocuments.net/reader034/viewer/2022042619/58802c491a28ab9f0f8b59f1/html5/thumbnails/36.jpg)
REFERENCES[1] Cavaliere , M; Csikasz, A; Jordan ,F. (2008 )Graph Transformations and Game Theory: A Generative Mechanism for Network FormationUniversity of Trento : 5
[2] Mankiw, G. (2012)Principles of MicroeconomicsCengage
[3] www.theincidentaleconomist.com
[4] www.eecs.umich.edu
[5] www-desir.lip6.fr
[6] www2.econ.iastate.edu
[7] www.dgca.nic.in
[8] www.goindigo.in
©Rupakshi Bhatia