Fuzzy Logic In Business Simulations

Fuzzy Logic

31 January 2015


A comparison between a fuzzy set and crisp set.
A comparison between a fuzzy set and crisp set. (Photo credit: Wikipedia)

The basic neural network algorithm deals with discrete data with values of known certainties. Thus the input values may be “light switch is turned ON” and “electric current supply is AVAILABLE” producing the output value “the lamp is LIGHTED”.

In the real world, the relationships between causes and effects are often not as clear cut. For example, what is the effect of reducing unit prices for automotives of a particular class on the market share of a certain age group, gender and income bracket? This question may be framed as follows.

  • OBJECTIVE (desired output) – Determine the change in market share
    • Change in unit price
    • Car class
    • Customer age group
    • Customer gender
    • Customer income bracket

Reducing the problem to deal with individual customers, we arrive at the need to define a fuzzy of customers who may be motivated to buy.

A normal data set is termed as crispy, referring to the fact that the clear cut boundaries defining the data set. Examples of crispy data sets are car classes, customer age groups, customer genders, and customer income brackets (at least for customers with steady fixed incomes). We know where a customer fits in in any of these sets given their age, gender and income. A fuzzy set, on the contrary, has vague boundaries, like a cloud with unclear edges, such as the dataset defining customers who are likely to purchase a particular car model. Thus some customers may be located near the center of the cloud where the density of the cloud is thick and the certainty of purchasing is high, while some customers may be located near the edge of the cloud where the density is lower and the certainty of buying is also low. What is the density distribution of the cloud – how thick it is in the center and the edges and how widespread are the edges – would determine how many people would be likely to purchase the car, and thus the change in market share following a change in prices.

In a simple game engine, such rules may be applied by entering estimated probabilities as input values for the likelihood to purchase. For a sophisticated game engine that aims to have a high degree of contextual insight representing a particular market, the probabilistic distribution graphs for the purchase likelihood would be statistically determined using past data. Price sensitivity is not the only factor determining purchase likelihoods. A combination of numerous factors that influence the purchase likelihood would be fed into a fuzzy neural network and through supervised or unsupervised machine learning, the dynamic interactions of these numerous factors as would occur in that particular real market context, would be determined and applied to the simulation. These would be used to define the fuzzy of people who are likely to make a purchase decision in that particular situation.

The following diagrams show some examples of fuzzy sets where members belonging to the dataset include partial values (ie person A belongs only partially say 60% to the set of people who will purchase). Since the graphs are triangular in shapes, they are normally referred to as T-norm graphs.


A fuzzy logic situation deals with ambiguities and uncertainties. These terms are defined as follows:

  • Crisp value – If the price is reduced by 10%, I will increase my purchase by 10 units
  • Ambiguity – If the price is reduced by 10%, I will purchase some additional units
  • Uncertainty – If the price is reduced by 10%, I may purchase some additional values

Application of actual historical data to the simulation using fuzzy neural networks will allow the game to simulate actual contextual market behavior within the game dynamics.

A number of these graphs are then used in combination through the application of fuzzy set operations to determine memberships in combined unions, overlapping intersections and excluded complements as shown below, and the results of these operations would be used to represent more complex market behaviors.


In the examples below, shades of grey are used to represent the density of probabilistic memberships in different sets


Combining fuzzy logic datasets with neural networks will allow the simulation engine to provide more realistic contextual insights to simulated games.

Neural Networks In Simulation Games

Neural Network

30 January 2015


Events and the dependencies between them may be represented using a form of artificial intelligence known as Artificial Neural Networks (ANN). ANNs are inspired by a simplified version of the brain consisting of neurons (represented by events) and synapses (represented by dependencies or connections between events). ANNs provide a flexible model that can be scaled up massively to represent complex simulations of the real world – it lends very well to parallel computing, uses memory and storage spaces in a structured and manageable manner, and may be compartmentally implemented in a modular manner within a distributed or server farm environment. Coupled with other aspects of the architectural design, the neural network may be combined with fuzzy logic, hierarchically multidimensional representations of data, persistent storage and etc to deliver the required features and functions of the game engine.

ANN offer a distinct advantage of other common ways of implementing simulation games such as the approach using arrays in Games of Life which are widely used in simple simulation games such as Sim City, or the relatively unstructured approach of defining rules, players and random seeds such as the Monte Carlo Method which can grow in an unwieldy manner when a simulation is scaled up to reflect more complex models. It expands on the basic ideas of Bayesian Belief Networks and provides a foundation to introduce value-added peripheral features to handle more complex models Wikipedia quotes:

Like other machine learning methods – systems that learn from data – neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

From <https://en.wikipedia.org/wiki/Artificial_neural_network>

Wikipedia introduces ANNs as


In machine learning, artificial neural networks (ANNs) are a family of statistical learning algorithms inspired by biological neural networks (thecentral nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected “neurons” which can compute values from inputs, and are capable of machine learning as well as pattern recognition thanks to their adaptive nature. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network’s designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

From <https://en.wikipedia.org/wiki/Artificial_neural_network>

Note that Wikipedia described ANNs as a family of algorithms, not a single algorithm. This reflects the fact that there are many types and variations of ANNs offering a rich variety of solutions to produce simulation games, for example ANNs that implement Back Propagations may be used to produce games that try to fit a simulation model from a gross estimation of a real world scenario into a closer fit of known historical reality, thereby discovering insights into relationships between influencing events that reflect the real world. In a real sense, the use of ANNs in simulation games allow not only learning by human students but also unsupervised or semi-supervised learning by machines, the outcome of which will reinforces human learning through the process of discovering hitherto unexpected insights or confirming intuitive expectations.

In particular the manner in which ANNs may be used to build up an arsenal of discoveries from a relatively small set of known assumptions in a non-linear fashion based on statistics lend itself very well in modelling complex, real world situations.

Wikipedia further describes ANNs as follows

A Simple 3-Layered Artificial Neural Network

Examinations of the human’s central nervous system inspired the concept of neural networks. In an Artificial Neural Network, simple artificial nodes, known as “neurons“, “neurodes”, “processing elements” or “units”, are connected together to form a network which mimics a biological neural network.

There is no single formal definition of what an artificial neural network is. However, a class of statistical models may commonly be called “Neural” if they possess the following characteristics:

  1. consist of sets ofadaptive weights, i.e. numerical parameters that are tuned by a learning algorithm, and
  2. are capable ofapproximating non-linear functions of their inputs.

The adaptive weights are conceptually connection strengths between neurons, which are activated during training and prediction.

Neural networks are similar to biological neural networks in performing functions collectively and in parallel by the units, rather than there being a clear delineation of subtasks to which various units are assigned. The term “neural network” usually refers to models employed in statistics, cognitive psychology and artificial intelligence. Neural network models which emulate the central nervous system are part oftheoretical neuroscience and computational neuroscience.

In modern software implementations of artificial neural networks, the approach inspired by biology has been largely abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks or parts of neural networks (like artificial neurons) form components in larger systems that combine both adaptive and non-adaptive elements. While the more general approach of such systems is more suitable for real-world problem solving, it has little to do with the traditional artificial intelligence connectionist models. What they do have in common, however, is the principle of non-linear, distributed, parallel and local processing and adaptation. Historically, the use of neural networks models marked a paradigm shift in the late eighties from high-level (symbolic) artificial intelligence, characterized by expert systems with knowledge embodied in if-then rules, to low-level (sub-symbolic) machine learning, characterized by knowledge embodied in the parameters of a dynamical system.

From <https://en.wikipedia.org/wiki/Artificial_neural_network>

In particular, advances in ANNs has led to the development of a particular field in artificial intelligence or knowledge discovery known as Deep Learning.

Wikipedia describes Deep Learning as follows:

Deep learning (deep structured learning or hierarchical learning) is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g. face recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between the stimulus and the neuronal responses and the relationship among the electrical activity of the neurons in the brain.

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, and deep belief networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.

Alternatively, deep learning has been characterized as a buzzword, or a rebranding of neural networks

From <https://en.wikipedia.org/wiki/Deep_learning>

ANNs has been widely used in the Medical and Robotics fields in particular due to its pattern recognition and discovery capabilities. The application of ANNs to simulate and predict real world business is also widely cited by numerous parties, such as the following:

Applications of Neural Networks

Neural Networks in Business and Economy

Many problems in business world are essentially about trying to predict the likelihood of different outcomes. The real world is so complex, with so many inter-related variables that predicting these outcomes are often very difficult. Conventional computing has a limited role to play in helping with these tasks but neural networks offer an alternate solution. By feeding in the different factors that affect an outcome over time a network can analyse previous trends and patterns to predict the future.

Predicting Stocks with Neural Networks

Warren Buffett is one of the most important and successful figures in the financial world. Others have built their own highly successful investment portfolios using his theories on investment and market analysis. Walkrich Investment Advisors used Neural Networks to produce an investment tool WRRAT based loosely on Warren Buffett’s ideas to predict stock prices, and determine which stocks are trading below their market value. The results from January 1995 to January 1996 showed that a Portfolio of WRRAT’s most under-priced shares saw an average advance of 33%.

Another example is the use of neural network software by LBS Capital Management to predict the S&P 500 index. The company uses an expert system to provide instructions to the neural network, which then processes the data accordingly. When tested with hundreds of previous days data the neural network LBS trained predicts the S&P 500 with an accuracy of about 95%.

Predicting Currencies with Neural Networks

O’Sullivan Investments successfully used many neural networks in order to advise them of market trends. Mr James O’Sullivan produced an article Neural Nets: A Practical Primer, AI In Finance, 1994 outlined some of the networks used. One of the most important factors in producing a successful net is to ask the right kind of question. Rather than simply ask what the projected price of a currency might be, he asks at what price the market is likely to take off in one direction or the other etc.

Predicting Natural Gas Prices with Neural Networks

Northern Natural Gas is a regulated wholesaler of natural gas. They must develop and file a rate for the gas they sell based on the average cost of the gas. By developed a neural network that use factors such as the quarter of the year, season, temperature last month etc. to predict the following months oil price, the company was better able to plan rates.

Predicting bonds with Neural Networks

  1. R. Pugh & Company does consulting to predict the prices of bonds of public utilities. The company used neural networks to help forecast the following years corporate bond prices and ratings of over 100 public utility companies. The network they used compared very favourably to conventional mathematical analysis. Whereas the network was able to predict a utilities rating (A, B, C) with 95% accuracy, conventional mathematical analysis was only effective 85% of the time. The only difficulties encountered by the network were associated with companies experiencing particularly unusual problems that were not incorporated into the networks inputs.

Targeting Direct Mail Marketing with Neural Networks

Microsoft used neural networks to maximise the effectiveness of their marketing campaign. Each year the company sent out mail to its registered customers. Most of this mail offered upgrades or new software but the response rate was rather low. The company used a neural network that was fed various variables such as how recently they registered, how many products they have bought etc. to target users more effectively. The results showed an average mailing lead to a 35% cost savings.

Credit Scoring with Neural Networks

Research conducted by Dr Herbert Jensen PhD demonstrated that “building a neural network capable of analysing the credit worthiness of loan applicants is quite practical and can be done quite easily”. The neural network was trained on no more than 100 loan applications to process application data such as occupation, years with employer etc. Despite the relatively small training set the network achieved a 75-80% success rate. This compared well with more traditional scoring methods that resulted in about a 75% success rate.

Real Estate Appraisal with Neural Networks

Several neural networks have been used to predict the sale prices of homes in order to help appraisers assess, sellers estimate asking prices, and home owners decide on improvements. Richard Borst successfully trained a neural network to appraise real estate in the New York area. His network incorporated almost 20 variables including the square feet of living area, age, etc. He used over 200 sales records from 1988 and 1989 to train the network with about 90% accuracy.

From <http://www.neuralnetworksolutions.com/nn/applications1.php>

Neural Networks in business

Business is a diverted field with several general areas of specialisation such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis. 

There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.


There is a marketing application which has been integrated with a neural network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a computer system made of various intelligent technologies including expert systems. A feedforward neural network is integrated with the AMT and was trained using back-propagation to assist the marketing control of airline seat allocations. The adaptive neural approach was amenable to rule expression. Additionaly, the application’s environment changed rapidly and constantly, which required a continuously adaptive solution. The system is used to monitor and recommend booking advice for each departure. Such information has a direct impact on the profitability of an airline and can provide a technological advantage for users of the system. [Hutchison & Stephens, 1987]

While it is significant that neural networks have been applied to this problem, it is also important to see that this intelligent technology can be integrated with expert systems and other approaches to make a functional system. Neural networks were used to discover the influence of undefined interactions by the various variables. While these interactions were not defined, they were used by the neural system to develop useful conclusions. It is also noteworthy to see that neural networks can influence the bottom line.

Credit Evaluation

The HNC company, founded by Robert Hecht-Nielsen, has developed several neural network applications. One of them is the Credit Scoring system which increase the profitability of the existing model up to 27%. The HNC neural systems were also applied to mortgage screening. A neural network automated mortgage insurance underwritting system was developed by the Nestor Company. This system was trained with 5048 applications of which 2597 were certified. The data related to property and borrower qualifications. In a conservative mode the system agreed on the underwritters on 97% of the cases. In the liberal model the system agreed 84% of the cases. This is system run on an Apollo DN3000 and used 250K memory while processing a case file in approximately 1 sec.

From <http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html>

A very comprehensive paper on applications of ANNs including in economics and business citing amongst others Quality Control, Market Analysis and Financial Forecasting and Portfolio Management (amongst numerous others) may be accesses from http://www-isl.stanford.edu/~widrow/papers/j1994neuralnetworks.pdf

The following paper provide some in depth examples of the application of ANNs in business. Although the examples do not apply ANNs to business simulations per se, it is quite easy to see how the same mechanisms may be applied to perform simulation games – https://www.google.com.my/url?sa=t&rct=j&q=&esrc=s&source=web&cd=9&ved=0CFUQFjAI&url=http%3A%2F%2Fwww.cse.hcmut.edu.vn%2F~dtanh%2Fdownload%2FANN_BusinessApplications.ppt&ei=8z3LVOH9FcHUmAX89YKoBw&usg=AFQjCNG8XKuZ5v6kF33rery0Jvm0I1fM8g&sig2=oHSWHTCevf9KOi–IvdUsQ&bvm=bv.84607526,d.dGY&cad=rjt

Features and Functions of Business Simulation Engines

The production of simulation games requires the development or acquisition of a suitable game engine that is configurable to produce specific simulations. Presented below are the key features and functions that need to be developed in the game engine.


  1. Events

Events in the context of the game engine should not be confused with events as we generally mean the term in everyday life. In the context of the game engine, events are quantitative data that are either decided upon by the game designers, system administrators, game players or automatically generated by the system either as a random occurrence or in response to other events. A close examination of the previous statement means that events are either input values (eg player decisions) or output values (eg resultant market share) or more typically both (eg player decides on increasing the sales budget which is an input by the player and an output to market share – this is just a simplistic example to illustrate the point).

The following scenario illustrates the mechanism of events. The actions decribed in the diagram occurs during one move or turn during the game. The player decides to increase his advertising budget, which is an event that carries a numerical value. The game has been configured by the games designer to carry the following dependencies – increasing the advertising budget immediately increases costs and reduces profitability in the short run, however it also increases market share which may increase sales and profitability in the long run. Thus the events described by diagram includes the following although various impacts are felt by each of the events during different time frames:

  • Advertising Budget
  • Costs
  • Profits
  • Market Share
  • Sales

In a realistic game engine with contextual insight, tens of thousands of events may be designed in the engine. Some events may be qualitative measures that are implemented as quantitative values, for example the propensity to spend on luxury goods in a particular income bracket may be defined as a value from 0 (unlikely) to 10 (certainty) with varying levels of propensity in between.

  1. Dependencies

Dependencies refer to the cause and effect linkages between events. In the above example, there is are dependencies between

  • Advertising Budget (cause) to Costs (effect)
  • Costs (cause) to Profits (effect)
  • Advertising Budget to Market
  • Market Share to Sales
  • Sales to profits

Dependencies are configured by the game designers during the game design process. In a realistic simulation game, events will have a huge number of dependencies (causes and effects) with one another.

  1. Clock

The clock is one of the internal mechanism used by game engine to determine the impacts realized through the dependency mechanism. Some impacts are realized immediately, while others may take time for realization and gradual realization over a curve characteristic over a period of time may also be built into the design.

  1. Dimensions

While events are quantitative values, dimensions are typically non-quantitative values used to further define the particular instance of an event. For example, dimensions applied to the Advertising Budget may be

  • Time (when is the increase in Advertising Budget incurred)
  • Product (to which product is the Advertising Budget applied)
  • Location (in which geographical region is the Advertising Budget applied)
  • Media (how is the Advertising Budget used, through which medium is the advertising campaign carried out)

Some dimensions may be quantitative values implemented as quantitative values, for example age of customers may be grouped into bands say pre-schoolers, schoolers, teenagers, young adults, married young adults, etc.

  1. Fuzzy Logic

Fuzzy logic is best described as what is not fuzzy logic. In a non-fuzzy logic situation a certain cause will definitely produce a certain effect, for example flicking on a light switch will certainly turn on the light. In a fuzzy logic situation, the impact of a certain cause to an effect is determined probabilistically, for example the impact of increasing an advertising budget may increase sales probabilistically. The probability of a cause causing and effect is determined by a probabilistic curve, and the characteristic of the curve is determined by the game designers during the game design phase. The choice of a curve by the game designers may involve a careful analysis of past market data, in which the game may exhibit a better reflect of the real market.

These probabilities are then applied to the game as weightages in the dependencies between events. Thus an event that has 100% weightage has an absolutely certain impact (eg increasing the Advertising Budget has 100% certainty of increasing costs) but dependencies with less than 100% weightage has varying impacts (eg increasing the Advertising Budget has a fuzzy impact on the Market Share.

  1. Predictive Analytics

Predictive Analytics refer to a special application of the above-mentioned game engine in which the game engine is used to determine the strength of dependencies between defined events. In most cases, all events in the game engine used for predictive analytics are defined as having dependencies with all other events. The game then is started using data that reflects the actual state of players at a real world point of time. Then, various strengths of weightages are applied to the dependencies by almost by trial and error until the model simulates actual real world events that had occurred. The exercise then concludes with dependency weightages that reflect historically how the real world behaves.

The main argument against predictive analytics is that it only predicts market behaviors as long as the market behaves consistently over time. That is to say, since the exercise uses historical data to predict future behaviors, the method only works if the future market behaves as it did in the past. In reality, a predictive analytics is defined using much input from the experience of the analytics designers. For example, the designers may choose historical trends that reflect the dominance of various age groups in the target market, knowing that the age factor is an important determinant in the particular market. This may be applied, for example, in simulating sales of fashion apparels.  On the other hand, the analytics designers may place emphasis on other historical market data in say, analysing the engine oil market.

  1. Constraints

Dependencies are not the only factors that determine the impact of cause events on effect events. Another set of factors that are also taken into account are constraints. For example, increasing the Advertising Budget may increase propensity to be sell, however the Market Size may be a constraint that limits growth potentials.

  1. Rules

Rules are determined for games to set the dos and don’ts of the game. For example, there may be prohibitions for sellers to sell shares in a bank beyond a certain percentage of shareholding to foreign entities. Rules are introduced into gaming engines to reflect real world dos and don’t’s in the marketplace.

The above required features and functions operate within another set of needs. These include infrastructural requirements to allow games to backed up and restored in case of disasters, responsiveness requirements to allow players to enjoy smooth playing experiences, user-friendly interfaces for game designers, administrators, instructors and players, the ability to roll-back and create what-if scenarios in midst games (a feature not provided by most existing gaming providers), the ability to analyse games and produce reports conducive for post-mortem analysis (a feature not provided by most existing game providers), etc.

Although the additional requirements, are not elaborated in detail, they are nevertheless vital requirements in the gaming eco-system.

Market Trends In Gamification Of Education

The use of games in the learning process through gamification of learning subjects has become a major market trend in the competitive training and education field where training organizations and institutions jostle with one another to provide higher quality of training while allowing students and trainees to learn through an efficient learning process that consumes their training time more effectively via the web and online methods.

From http://elearninginfographics.com/gamification-in-elearning-infographic

Over 75% people are gamers (50% casually and 27% moderately to fairly often). Learners recall just 10% of what they read and 20% of what they hear. If there are visuals accompanying an oral presentation, the number rises to 30%, and if they observe someone carrying out an action while explaining it, 50%. But learners remember 90% “if they do the job themselves, even if only as a simulation.

  1. Almost 80% of the learners say that they would be more productive if their university/institution or work was more game-like.
  2. Over 60% of learners would be motivated by leader boards and increased competition between students.
  3. 89% would be more engaged win an e-learning application if it had point system.

Favorite Gamification Techniques

  1. Progressing to different levels
  2. Scores
  3. Avatars
  4. Virtual Currencies

Less Favorite Gamification Techniques

  1. Competition with friends
  2. Virtual Gifts
  3. Being Part of a narrative (so called “interactive fiction”)
  4. Real time performance feedback and activity feeds

The Most Effective Uses of Gamification in Learning

  1. Illustrating progress
  2. Increase engagement
  3. Creating challenges
  4. Instilling a sense of accomplishment
  5. By 2015 50% of organizations managing innovation processes will gamify aspects of their business.
  6. Accordingly, by 2015, 40 percent of Global 1000 organizations will use gamification as the primary mechanism to transform business operations.
  7. 53% of responders say that by 2020 gamification will be widely adopted by most of industries, communications scene and most of all education.

Contextual Insight

While a number of business simulation games providers deliver off-the-shelf packages suited for particular industries, these games are not set into particular realities of specific markets. For example, games that provide business simulation in the energy industry provide generic frameworks and train students to manage their businesses in a generic energy industry context. They do not reflect the realities of operating energy producers in say specific production areas such as the relatively new African oildfields (with their own risk profiles and uncertainties) as opposed to the mature and stable oilfields in Saudi Arabia and Kuwait.

The ability to mimic characteristics of a particular market in a business simulation game will deliver a great additional value to students and trainees. This value is called contextual insight, referring to an insight into the context of a particular marketplace. In many instances, simulating a business game within a particularly targeted marketplace profile reveals unanticipated results during a business simulation, allowing students to bring back not only a firmer grasp of the theoretical concepts that they have learnt in the classroom but also a more profound understanding how those concepts may work in their own marketplaces in respond to various scenario and competitive landscape changes. In fact, many top executives with deep understanding of theoretical concepts having applied them through years of experience will still appreciate the ability to simulate the application of these concepts in a simulated marketplace mimicking their own environments due to the insights that may be gained from the exercise.

The Malaysian context, for example, have its own peculiar characteristics for example influence and control over companies owned by various key groups such as government-linked companies, multi-national entities, privately owned corporations exhibit different behavioral responses and rapidity in the marketplace. For example, in the retailing business consumers face alternatives ranging from government-linked companies (Tesco), multi-national companies (Carrefour), privately owned corporations (Mydin), retail chains (99 Speedmart), and local grocers, each of which have different profiles of risk appetites, rapidity of decision making, market offerings, supplier policies, etc. The ability to simulate a particular key event into the market, such as increased consumer costs due to the introduction of Government Sales Tax, would allow not only freshmen students but also season top executives to simulate various responses and reflect upon their anticipated outcomes, and thereby bring back to their offices fresh insight from the business simulations.

The lack of these contextualized simulations in the offerings of simulations and games providers and the demand for them present an opportunity gap for new simulated game providers. The provision of specifically contextualized games may require a massive re-architecturing of existing game engines that were designed to deliver a different level of gaming experience, for example in the area of defining dependencies between influencing factors, such that it may provide an even playing field between existing and new gaming services providers. Furthermore, most gaming service providers target their products for the broader generic online enrolment market with less concentration on customized gaming development, thereby the shift in focus to provide customized contextualized gaming to specific markets may not be aligned with their business emphasis.

Games and Simulations in Education

Games and simulation has been employed in education to provide a platform that allows students to put theories into practice in an environment that closely mimics the real world without incurring real world costs. Typically, simulated games pit individual students or teams of students against other student teams and teams within a simulated environment with the aim of maximising a certain outcome. Examples of simulation games applied to education includes the following:

  • Business Games

Students/ teams manage companies that compete with one another to maximise a pre-determined target which may be market share, company book net worth or more typically company market capitalization value. Students apply a broad range of business management concepts in diverse areas from product design, marketing management,  operations management, capital investments and financial management to achieve an optimum level of benefits against trade-offs such as costs within a range of constraints such as availability of capital and within environmental determinants such as economic growth and inflationary pressures.

  • Portfolio Management

Students/ teams manage investment portfolios that include investments in companies (without running the micro operations of the companies), invest idle surplus funds in market instruments, borrow from the capital markets, and optimize a liquid near cash fund with a view of maximising the value of the managed funds. Actions by individual players and teams affect the availability and cost of funds in the market, and investment decisions lead to optimal investment policies that determine the growth of the managed funds.

  • Economic Management

Students/ team determine economic policies of countries within a competitive global environment to achieve maximum economic growth for their managed nations. These policies include fiscal and monetary policies such as taxation types and rates (which provide government revenue but suppress business interest), spendings on key government budgets such as infrastructure and capacity building, education (to produce a workforce), immigration policies (quick workforce availability but drainage on foreign exchange), etc.

  • Political Influence

Students/teams pit with one another to influence the greatest influence over the voters population as indicated by periodic polling and election results. Factors include, for example, increasing government spendings on voting concerns and areas that has the greatest impact on certain target voters sector while raising unpopularity on other targeted voter concerns and areas (eg increasing spending on unemployment benefits is popular with the unemployed sector but unpopular with the tax paying sector).

Oft-cited benefits of game simulation in education include the following

  • Faster understanding and longer retention of taught concepts due to the engaging nature of the game simulations.
  • Higher interest to study amongst students and the opportunity to experience how their classroom training is relevant and applicable within a simulated real world environment
  • Increase in students’ ability to apply difficult theoretical concepts in real world workplaces, such as optimizing financial leveraging vis-a-vis business certainty and risks. Employers cite profitable returns in education and training investments due to this factor.
  • Educational institutions cite greater productivity in the use of lecturers’ time and allocation of lecturing resources due to the lower lecturer to student ratio required during gaming exercises despite the fact that the same exercise produce better student absorption of trained material as opposed to traditional or passive teaching methods

Games and simulations are widely used in education and training in progressive and leading business schools. Additionally, they are increasingly being used by corporations and other organizations to enhance their internal training and executive development programs.