scholarly journals Game theory modelling of retail marketing discount strategies

2019 ◽  
Vol 37 (5) ◽  
pp. 555-566
Author(s):  
Mark Taylor ◽  
Vince Kwasnica ◽  
Denis Reilly ◽  
Somasundaram Ravindran
Keyword(s):  
Game Theory ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Modelling ◽  
Marketing Strategies ◽  
Theoretical Research ◽  
Consumer Decision Making ◽  
Content Type ◽  
Retail Marketing ◽  
Payoff Matrices

Purpose The purpose of this paper is to use the game theory combined with Monte Carlo simulation modelling to support the analysis of different retail marketing strategies, in particular, using payoff matrices for modelling the likely outcomes from different retail marketing strategies. Design/methodology/approach Theoretical research was utilised to develop a practical approach for applying game theory to retail marketing strategies via payoff matrices combined with Monte Carlo simulation modelling. Findings Game theory combined with Monte Carlo simulation modelling can provide a formal approach to understanding consumer decision making in a retail environment, which can support the development of retail marketing strategies. Research limitations/implications Game theory combined with Monte Carlo simulation modelling can support the modelling of the interaction between retail marketing actions and consumer responses in a practical formal probabilistic manner, which can inform marketing strategies used by retail companies in a practical manner. Practical implications Game theory combined with Monte Carlo simulation modelling can provide a formalised mechanism for examining how consumers may respond to different retail marketing strategies. Originality/value The originality of this research is the practical application of game theory to retail marketing, in particular the use of payoff matrices combined with Monte Carlo simulation modelling to examine likely consumer behaviour in response to different retail marketing approaches.

Risk assessment of construction projects using Monte Carlo simulation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abroon Qazi ◽  
Mecit Can Emre Simsekler
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Attitude ◽  
High Impact ◽  
Risk Exposure ◽  
Risk Matrix ◽  
Content Type ◽  
Simulation Based ◽  
Project Risks ◽  
Low Probability

PurposeThis paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments.Design/methodology/approachThis paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry.FindingsThe proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone; however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach.Originality/valueMonte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

OCTO SCM: assessment of determinants of seaport logistics for the export supply chain of agricultural products

2021 ◽  
Vol 11 (3) ◽  
pp. 1-18
Author(s):  
Raj V. Amonkar ◽  
Tuhin Sengupta ◽  
Debasis Patnaik
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Supply Chain ◽  
Learning Outcomes ◽  
Agricultural Products ◽  
Supply Chain Design ◽  
Content Type ◽  
Focus Session ◽  
Academic Level ◽  
Design Structure

Learning outcomes The learning outcomes of this paper are as follows: to understand the context of seaport logistics and supply chain design structure, to apply Monte Carlo simulation in the interface of the supply chain and to analyze the Monte Carlo simulation algorithm and statistical techniques for identifying the key seaport logistics factors. Case overview/synopsis It was 9:00 p.m. on November 10, 2020, and Nishadh Amonkar, the CEO of OCTO supply chain management (SCM) was glued to the television watching the final cricket match of the Indian Premier League, 2020. Amonkar’s mobile phone rang and it was a call from Vinod Nair, a member Logistics Panel of Ranji Industries Federation. Nair informed Amonkar that it was related to the rejection of several export consignments of agricultural products from Ranji (in the western part of India). The rejection was due to the deterioration in the quality of the exported agricultural products during transit from Ranji to various locations in Europe. Complexity academic level This course is suitable at the MBA level for the following courses: Operations research (Focus/Session: Applications on Monte Carlo Simulation). SCM (Focus/Session: Global SCM, Logistics Planning, Distribution Network). Logistics management (Focus/Session: Transportation Planning). Business statistics (Focus/Session: Application of Hypothesis Testing). Supplementary materials Teaching Notes are available for educators only. Subject code CSS 9: Operations and logistics.

Testing for bubbles in housing markets: some evidence for Brazil

2018 ◽  
Vol 11 (5) ◽  
pp. 754-770 ◽  
Author(s):  
Cássio da Nóbrega Besarria ◽  
Nelson Leitão Paes ◽  
Marcelo Eduardo Alves Silva
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Housing Prices ◽  
Market Price ◽  
Housing Markets ◽  
Cointegration Analysis ◽  
Small Samples ◽  
Content Type ◽  
Long Run ◽  
Cointegration Tests

Purpose Housing prices in Brazil have displayed an impressive growth in recent years, raising some concerns about the existence of a bubble in housing markets. In this paper, the authors implement an empirical methodology to identify whether or not there is a bubble in housing markets in Brazil. Design/methodology/approach Based on a theoretical model that establish that, in the absence of a bubble, a long-run equilibrium relationship should be observed between the market price of an asset and its dividends. The authors implement two methodologies. First, the authors assess whether there is a cointegration relationship between housing prices and housing rental prices. Second, the authors test whether the price-to-rent ratio is stationary. Findings The authors’ results show that there is evidence of a bubble in housing prices in Brazil. However, given the short span of the data, the authors perform a Monte Carlo simulation and show that the cointegration tests may be biased in small samples. Therefore, the authors should be caution when assessing the results. Research limitations/implications The results obtained from the cointegration analysis can be biased for small samples. Practical implications The information on the excessive increase of the prices of the properties in relation to their fundamental value can help in the decision-making on investment of the economic agents. Social implications These results corroborate the hypothesis that Brazil has an excessive appreciation in housing prices, and, as Silva and Besarria (2018) have suggested, this behavior explains, in part, the fact that the central bank has taken this issue into account when deciding about the stance of monetary policy of Brazil. Originality/value The originality is linked to the use of the Gregory-Hansen method of cointegration in the identification of bubbles and discussion of the limitations of the research through Monte Carlo simulation.

Robustness-based multi-objective optimization for repetitive projects under work continuity uncertainty

2020 ◽  
Vol 27 (10) ◽  
pp. 3095-3113
Author(s):  
Lihui Zhang ◽  
Guyu Dai ◽  
Xin Zou ◽  
Jianxun Qi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Negative Impact ◽  
Project Performance ◽  
Project Managers ◽  
Multi Objective Optimization ◽  
Negative Effects ◽  
Content Type ◽  
Multi Objective ◽  
New Type

PurposeInterrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.Design/methodology/approachThis paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.FindingsThe results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.Originality/valueAvailable studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.

Application of life cycle assessment and Monte Carlo simulation for enabling sustainable product design

2014 ◽  
Vol 12 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Sekar Vinodh ◽  
Gopinath Rathod
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Product Design ◽  
Sustainable Product Design ◽  
Content Type ◽  
Sustainable Product ◽  
Manufacturing Organization

Purpose – The purpose of this paper is to present an integrated technical and economic model to evaluate the reusability of products or components. Design/methodology/approach – Life cycle assessment (LCA) methodology is applied to obtain the product’s environmental performance. Monte Carlo simulation is utilized for enabling sustainable product design. Findings – The results show that the model is capable of assessing the potential reusability of used products, while the usage of simulation significantly increases the effectiveness of the model in addressing uncertainties. Research limitations/implications – The case study has been conducted in a single manufacturing organization. The implications derived from the study are found to be practical and useful to the organization. Practical implications – The paper reports a case study carried out for an Indian rotary switches manufacturing organization. Hence, the model is practically feasible. Originality/value – The article presents a study that investigates LCA and simulation as enablers of sustainable product design. Hence, the contributions of this article are original and valuable.

A theoretical model of jump diffusion-mean reversion

2017 ◽  
Vol 23 (3) ◽  
pp. 537-554
Author(s):  
Anindya Chakrabarty ◽  
Zongwei Luo ◽  
Rameshwar Dubey ◽  
Shan Jiang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Model ◽  
Interest Rate ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Fixed Income ◽  
Short Term ◽  
Content Type ◽  
Better Than

Purpose The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures. Design/methodology/approach The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique. Findings Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors. Originality/value The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

Long-range dependence in Indian stock market: a study of Indian sectoral indices

2014 ◽  
Vol 9 (4) ◽  
pp. 505-519 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
Long Range ◽  
Time Domain ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Simulation Experiments ◽  
Content Type

Purpose – The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments. Design/methodology/approach – The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices. Findings – The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature. Originality/value – This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

A dynamic evolutionary game model of web celebrity brand eWOM marketing control strategy

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mengjie Liao ◽  
Jian Zhang ◽  
Ruimei Wang
Keyword(s):  
Game Theory ◽  
Social Media ◽  
Evolutionary Game Theory ◽  
Government Policy ◽  
Evolutionary Game ◽  
Theoretical Research ◽  
Dynamic Visualization ◽  
Formal Approach ◽  
Content Type ◽  
Marketing Environment

PurposeThis paper aims to recognize whether government policy supervision or social network platform supervision can effectively promote the control of misconducts of web celebrity brand eWOM marketing and to identify the key factors influencing the unhealthy web celebrity marketing environment.Design/methodology/approachTheoretical research was employed to develop a practical approach for applying evolutionary game theory to eWOM marketing controlling strategies modeling via dynamic visualization, systematic simulation experiments.FindingsEvolutionary game theory combined with dynamic simulation modeling can provide a formal approach to understanding web celebrity brand eWOM marketing decision-making in social media, which can thus support the control of unhealthy web celebrity marketing environment. The results demonstrate that the reasonable control of social platform control costs may be more effective than the government policy on web celebrity fake brand eWOM marketing behaviors.Originality/valueThe study enriches the research on the management and control of eWOM marketing as well as provides guidance for the sustainable development of the web celebrity economy in social media.

A game theory approach to selecting marketing-mix strategies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mansour Abedian ◽  
Atefeh Amindoust ◽  
Reza Maddahi ◽  
Javid Jouzdani
Keyword(s):  
Game Theory ◽  
Nash Equilibrium ◽  
Marketing Strategy ◽  
Equilibrium Model ◽  
Marketing Mix ◽  
Marketing Strategies ◽  
Theory Approach ◽  
Market Place ◽  
Content Type ◽  
The Game Theory

PurposeAdopting efficient marketing strategies is a challenging task in a competitive market place involving complex marketing planning, techniques and mechanisms to identify the best course of action under these circumstances and finding optimal solutions or stable outcomes. Decisions and strategies of competitors in the market influence the selection of the appropriate marketing strategy. The main purpose of this paper is to develop a mathematical methodology based on the game theory approach for planning optimal marketing-mix strategies in dynamic competitive markets, taking into account strategic foresight and interaction effects.Design/methodology/approachThe game theory approach, as a decision-making tool in conflict situations, is suggested for planning and adopting optimal marketing strategy. The main intellectual attraction of the game theory is essentially a question of how to act in gaming situations against highly rational opponents A kind of static, finite and non-cooperative game analytics approach has been developed for this issue, and the proposed model has been implemented to design optimal marketing strategies for two top brands of the automotive parts market in Iran.FindingsThe findings of this study show that the optimal marketing-mix strategy for brand A is pricing and for brand B is the product strategy.Practical implicationsGame theory and the Nash equilibrium model can provide a practical approach to find and adopt the right strategy, know competitors' movements and strategies and get more profit.Originality/valueThe integration of the game theory approach into the marketing mix framework has been adopted as a generalized model for marketing strategy planning and analysis as well as to resolve some shortcomings of the marketing mix framework. The Nash equilibrium model has been used to analyze the results. The incorporation of game theory into marketing models has the potential to enrich the scope of marketing modeling.

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