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Working Papers | 2016

Information Utility based Decision Support Framework

Manish Aggarwal

We introduce a novel entropy framework
for the computation of utility on the basis of an agent's subjective evaluation of the granularized information source values. A concept of evaluating agent as an information gain function of this entropy framework is proposed, which takes as its arguments both an information source value and the agent's evaluation of the same. A method to determine the agent's perceived utility values is also developed. Based on these values, several
new utility measures are designed for the valuation of the information source values, perceived utilities, and the evaluating agent.

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Working Papers | 2016

Learning Decision Models with Multinomial Logit Model through Pair-wise Preferences

Manish Aggarwal

Our goal is to study the behavioral process of a decision maker (DM) that leads to his choice.
To this end, we combine the established models of discrete choice with the recent algorithmic
advances in the emerging field of preference learning. Our proposed model takes the learning
information in form of the exemplary preference information, as revealed by a DM, and returns
the DM's choice probability. To accomplish our learning objective, we resort to the probabilistic
models of discrete choice and make use of the maximum likelihood inference. First experimental
results on suitable preference data suggest that our approach is not only intuitively appealing and
interesting from an interpretation point of view but also competitive to state-of-the-art preference
learning methods in terms of the prediction accuracy.

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Working Papers | 2016

Soft Information Set and its Application in Multi Criteria Decision Making

Manish Aggarwal

An information source value is perceived
differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the
notion of relations in SIS is devised. SIS has potential to facilitate multi-criteria decision making (MCDM) under uncertainty, involving different information source values and agents. Its usefulness as a uncertainty representation
tool in MCDM is illustrated through case-studies.

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Working Papers | 2016

A Game Theoretic Approach to Community based Data Sharing in Mobile Ad hoc networks

Premm Raj H. and Kavitha Ranganathan

Government interventions on usage of free speech
for communication has been rising of late. The government of Iraq's ban on the Internet, ban of mobile communications in Hong Kong student protests highlight the same. Applications
like Firechat which use mobile ad hoc networks (MANETs) to enable off the grid communication between mobile users, have gained popularity in these regions. However, there have been limited studies on selfish user behavior in community
data sharing networks. We wish to study these data sharing communities using game theoretic principles and propose a normal form game. We model selfishness in community data sharing MANETs and define the rationality for selfishness
in these networks. We also look at the impact of altruism in community data sharing MANETs and address the issue of minimum number of altruistic users needed to sustain the MANET. We validate the novel model using exhaustive
simulations and empirically derive important observations.

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Working Papers | 2016

Web Content Analysis of Online Grocery Shopping Web Sites in India

Arindam Banerjee and Tanushri Banerjee

In this paper the authors evaluate online grocery shopping web sites catering to customers primarily in India. The process of evaluation has been carried out in 3 parts; by comparing the web content on their homepages, analysing customer reviews and also analysing their business performance as summarized on public web sites that use search optimization tools and analytical processes. This paper aims to study attributes from structured and unstructured data that lead to success of online grocery business in India. Results of the study will help identify the keywords that Indian consumers prefer to use while searching for information on online grocery websites. It will also identify consumer preferences from the customer review analysis. Additionally, it will identify the parameters from web site traffic metrics that drive per day revenue for the online retailer.

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Working Papers | 2016

Learning of Utilitarian Decision Model through Preferences

Manish Aggarwal

Understanding and predicting the decision making behaviour of individuals is a
subject of interest for marketers, strategists, economists and the computer scientists
alike. We develop an aproach to learn a decision maker (DM)'s behavioral process
by combining recent possibilistic discrete choice models with the emerging machine
learning methods. The proposed approach considers the utility values derived by a
DM from each of the attribute values (information source values). We take the
training information in the form of the exemplary multi-attribute preferences, and
the decision model is specified in terms of two vectors that are unique to a DM. The
experimental results on a set of 10 benchmark datasets suggest that our approach is
both intuitively appealing and competitive to state-of-the-art methods in terms of the
prediction accuracy.

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Working Papers | 2016

Discriminative Aggregation Operators for Multi Criteria Decision Making

Manish Aggarwal

A general aggregation formalism for multi criteria decision making (MCDM) applications is presented that allows us to represent the existing aggregation operators as well as generate the new ones. Using this formalism, we develop new discriminative aggregation operators for aiding MCDM. Four families of the proposed discriminative aggregation operators are developed and applied in a managerial decision making application.

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Working Papers | 2016

Review of Marketing / Business Analytics Infrastructure in SELECT India-based Organizations

Arindam Banerjee

The paper provides details of a detailed survey carried out in five Indian organizations operating in different industries-oil and gas, food products, stock trading, hospitality and industrial chemical. Diversity of industry was maintained to ensure some level of representativeness of the study to the typical India based organization. Study pertained to both the marketing and operations functions and was focused on how business data was managed and used for decision making.

One unique feature about these organizations was that none of them were of the profile of an active Analytics driven organization which would have a natural appetite for data science and data mining output. This was a deliberate strategy to the direct the study beyond the existing boundaries of what is perceived to be an "Analytics focused organization".

The major finding is that unlike popular perceptions, most organizations have a repository of useful information that can be utilized to support decision making. However, there are structural constraints such as top management commitments, culture, internal power and control, perceived value of analysis, near term priorities and exigency that impede organizational capabilities of developing processes for better analytical output. Also, focus seems to be erroneously targeted on analytical techniques and not on evaluating information value to decision making.

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Working Papers | 2016

Rough Information Sets

Manish Aggarwal

The information sets are recasted as information granules by using the fuzzy equivalence relations. The proposed information granules can be visualized as the entropies corresponding to the information source values, drawn together by their similarity. The information granules are then further used to articulate rough information sets (RIS). The RIS are particularly useful in the approximation of decision concepts in terms of entropies (information). The usefulness of the rough information sets is demonstrated through a case-study.

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Working Papers | 2016

Attitudinal Choquet Integrals for Strategic Decision Making

Manish Aggarwal

The compensation capabilities of Choquet integral are augmented by providing it with an additional parameter to relate the same with the complex attitudinal character of a decision maker (DM). The resulting operator is termed as attitudinal Choquet integral (ACI). ACI operator is further generalized to develop generalized attitudinal Choquet integral (GACI) that represents an exponential class of ACI operators. The special cases of ACI and GACI operators are investigated. The variants of ACI and GACI operators, termed as induced ACI and induced GACI, are introduced. The proposed operators promise a great potential in modelling any human aggregation process that inevitably characterizes the individual attitude of a DM, criteria importances, and interaction among the criteria at the same time.

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