Individual tariffs for Mobile Communication Services: A Computational Model

1. Individual tariffs for Mobile Communication Services: A Computational Model Hong Chen, L-F Pau Rotterdam School of Management /ERIM {hchen, lpau} @ rsm.nl

2. Definitions • Mobile services: those that are provided through a mobile or wireless user equipment, through a ubiquitous connection to facilitate communications anytime anywhere, between human beings, between humans and machines and between machines. • Individualtariffs in telecommunications refer to the regulatory protected ability for an identified user to obtain from a service provider, by a bilateral specific contract, a set of service specific prices corresponding to a request or a proposal from the user specified with a service demand profile and some duration.

3. Incentives for individual tariffs Sociological perspective • Individualism • Self-identity • Innovation • Recognition Economics perspective • Price discrimination • Willingness-to-pay • Risks

4. User Behaviours (I) • Bounded rationality Model construction under bounded rationality can take two approaches: • To retain optimization, but to simplify sufficiently so the optimum is computable. -- heuristic principles --attribute substitution • To construct satisficing model which provides decisions good enough, with reasonable computational cost • Social Dimension A user’s preferences are affected by context & content of the communication service. • Context -- social location (i.e. public space such as a theatre, private space such as a friends home) -- social relationships (plus the effect of absent present) • Content -- time critical vs. non time critical (implies a hyperbolic discount function on time ) -- directly motivated vs. indirectly motivated

5. User behaviour (II): A Perceptual Space A perceptual space is • constructed by perceived attribute of a service • the perceived attributes are a reduced mapping from the technical attributes, based on certain heuristics or as a result of a learning process. • the number of attributes is significantly less than the number of technical attributes. (Usually 3-5) A Service design space / explicit space (of a supplier ) is • a space that is constructed by technical attributes of a service • the number of attributes can be large (10-50) • it is difficult for a normal user to understand every technical details versus

6. User Behaviour (III) User Utility: is a function of perceived attributers • Here we assume the user has in mind an idea set of attributes and their values, call it a target point. • User’s utility function is defined as a function of the Euclidian distance from the best possible point he can reach (when negotiating with the supplier) to his target point. User optimizes his utility by negotiating with the supplier(s) repeatedly

7. Supplier Behaviors Firms (e.g. mobile operators) Maximize economic benefits Communities Achieve financial breakeven and minimize service provisioning risks

8. Computational model (I): A mapping between user’s perceptual space and supplier’s explicit space • Suppose users can be divided into groups which share similar preferences for a specific class of services. • Conduct survey on a group of user and ask them about their preferences on “technical attributes” • Conduct Principle Component Analysis (PCA), the PCA loading can be seen as a mapping between the user’s perceptual space and the supplier's explicit space • Interpretation of PCA components is service dependent

9. User constraints: time, budget, user specific preferences Computational model (II): targets and constrains User target function User optimizes in perceptual space, subject to constraints which are expressed in the explicit space Assume supplier here is an operator Operator target function Profit = revenue – cost Operator optimizes in service design space, subject to constraints which are expressed in the explicit space Operator constraints: guarantee quality of service

10. Computational model (III): Negotiation process: A user lead recursive Stackelberg game Step 0: supplier advertises the public offering (denoted as x_ offer0) public offering is translated into z_offer0 individual user sets his target values for the perceptual attributes Step 1: user optimizes in z space, under his own constraints and taking into consideration the supplier’s offer. The results are translated into x space . Step 2: user makes decision and communicates with the supplier Step 3: supplier updates his constraints regarding the proposed value; then calculates his own optimum under the updated constraints. Supplier then makes decision and communicates with the user. Recursion and Stopping rules: the procedure repeats from (1)--(3) until it satisfies one of the following conditions:

11. Computational model (IV): Stopping rules User stops on either one of the two criteria: • the distance from one of the optimization results to his target point in Z space (user utility) is less then a relative value when compared to the initial situation. (e.g. less than 5% of the initial distance). • when the difference between two consecutive optimization results (user utilities) is less than a relative value when compared to the initial situation. (e.g. less than 0.1 % of the initial distance) Operator: Operator can either accept the request from the user and sign the contract or quit the game without signing the contract. Equilibrium: An EQ is defined as: the user wants to stop the negotiation and the operator makes positive profit.

12. A Numerical case: A mobile singing classroom service Service attributes: service design space

13. Service attributes: perceptual space PCA results Interpretation of generated PCA components 1st component: Price performance; 2nd component: Raw content (content same to everyone) in terms of quantity vs. quality 3rd component: Personalized content. Teaching intensity in terms of singing techniques,

14. Preliminary results

15. Thank you very much!

16. PCA Mapping PCA need to use normalized vector x to compute PCA loadings Denote attributes in service design space as a vector We use the first three PCA components in our calculation, denoted as Denote attributes in perceptual space as vector Denote PCA loadings as P Denote the first three PCA loadings as P* The linear transformation of PCA can be represented by The reduced linear transformation of PCA can be represented by Denote the pseudo inverse of P* as P-1 We have

17. A software implementation Matlab/toolbox + NGA toolbox