Dr. Richard Teach is a Professor Emeritus at Georgia Institute of Technology, where he taught in the Business School for 35 years. He has recently taught simulation design for the ISAGA Summer Schools in Venice, Italy (2007), Deli, India (2009), Cluj-Napoca, Lithuania (2009) and Paramaribo, Suriname (2010).
He was an Associate Editor for Simulation & Gaming: an International Journal of Theory, Design and Research for more than 10 years.
Dr. Teach has delivered over 100 presentations and lectures on simulations and games in 23 countries on five continents and has published over 100 peer reviewed articles the same subject. He was a featured lecturer at the annual Internationales Planspiel Forum (held in Bad Neuenahr, Germany) from 1987 to 1994, and he was the Key Note speaker at the first Austrian Planspiel Kongreß held in Vienna, Austria in 1993. Professor Teach has been an invited speaker on business simulation development at Fukuoka University (1993), Delft University (1993), University of Tokyo (1997), Jagiellonian University, Krakow (2006,7 & 8), and Queen’s University, Belfast (2007). His wide experience provides him with a unique perspective on simulation and gaming.
Q1: You have recently conducted research about Entrepreneurship: A game of risk and reward (Phase 2: The Start-up). Can you please summarize your findings?
Yes, I have done so during designing the game ENTREPRENEURSHIP (really 3 sequential games).
I was interested in what are typically called rapid growth, technology based start-ups. What I found was that the task of entrepreneurship for these types of firms can be broken into essentially 3 unique and separable tasks. There were:
1) The Search for Opportunity
2) The Start up stage
3) Market Entry
The Search for Opportunity is a task that an entrepreneurial or entrepreneurial team undertakes when deciding whether or not to start an entrepreneurial firm. As I have structured the game, the player(s) sees a technology based opportunity and needs to evaluate its potential. The player(s) may seek information about the opportunity such as its expected value proposition, a unit sales potential estimate, expected future manufacturing costs (in the form of an expected learning curve, etc). At the end of a short evaluation period, the player must decide either to take this opportunity and create a new firm or to pass on this opportunity. If the player chooses to accept this opportunity, she/ he moves to the start-up stage and never sees another opportunity. If the player decides to pass on this opportunity then sees another opportunity. The data presented to the player(s) is in the form of probability distributions. That is the player sees 3 points of a 100 observation non-symmetric distribution; the 10th percentile; the median or the 50th percentile point and the 90th percentile point. The Search for Opportunity uses a simulated week as its decision cycle for evaluating each opportunity.
Once the player(s) moves to the start-up stage, the nature of the business strategy games changes to a time management game. At this stages used a simulation time of one month as its decision cycle.
First, the player decides how much time they expect to spend working for the new start-up.
Then, they select and prioritize a set of tasks that they expect to accomplish during the first month. The priority system uses only 3 priorities. Priority 1 tasks will always be completed regardless of the amount of time it actually took. If there is any time remaining after priority 1 tasks are completed, priority 2 tasks are attempted, and if there is still time remaining, priority 3 tasks are attempted. The game generates actual time requirements according to a non-symmetric probability distribution. As expected, tasks generally take more time than one expects.
This start-up phase continues until the firm completes the business plan (needed before the player(s) can meet capitalists to obtain additional funding), the development of the product (product development needed before final funding for the production of testable products may begin), the completion of alpha and a beta market test (needed before the new product can be sold), initial sales of a few units to contacts of the entrepreneur, (producing the first sakes income for the new firm, and the completion of a marketing plan (needed before the product can be sold in any volume.
The final stage – Market Entry is then simulated using a 3-month decision cycle and the firm’s first competitive reactions are encountered.
The ENTREPRENEURSHIP game is designed to allow the players to conduct an autopsy, if it fails. In general, this autopsy activity provides the intended learning experience of the game. It is intended to have the player(s) better understand “why firms fail.”
Q2: Your game has a special twist in it, namely that it teaches what to learn from inevitable failure as opposed to success. Can you tell me why have you chosen this angle to simulate?
Most, if not all, business simulations are designed in a way that the players never fail. As a result, players often feel that they “know how to run a firm” after completing business simulator games and this feeling is enhanced when a particular team “wins the game”. This feeling is especially enforced by game authors who often write in their manuals how realistic their games are. But actually, realism is never present in business games.
I wanted to reverse this process and make the players face a variety of problems. After all, depending on when one starts the calculations, 80 to 95% of new ventures fail and a lot of valuable information can be gleaned from analyzing a failed firm, even a failed simulated firm. The important link to learning is the analysis of causality. As a result the players should be more cautious and do more analysis in future potential ventures.
Q3: Do you have any advice for those who are looking for ways to challenge their students in the classroom?
Students are almost always looking for challenges. Most of us do not challenge our students in ways that provide both learning opportunities and provide some measure of fun to the experience. I am convinced, by observations, that good teaching involves a substantial combination of learning and fun.
Most students today suffer though “Death by PowerPoint” and even in case analysis, it is often mote lecture than discussion except at those few programs that utilize cases extremely well and this is a very small proportion of the business schools around the world.
Simulations do not need to have 50 to 100 (and some use more) decisions per round and require 15 to 20 decision periods. Game complexity often destroys the learning process. It has been definitively shown that humans can only process 6 to 8 dimensions, and most students without extensive experience can only handle 4 or 5.
Good simulations do not all to have a large number of decision periods. Some are designed so that 3 to 5 decision periods are able to convey memorable and teachable concepts.
Q4: What is your opinion on the current debate regarding student learning assessment when it comes to business simulation games? How do you assess and grade your students’ performance?
This is really the major issue for any teaching methodology and is clearly an important issue that all gamers need to address. There are several concepts being currently evaluated. One is to use business simulators in a capstone experience as a way to assess the learning that takes place during a “B” school education. But, to believe that these business games have a strong relationship to prior learning, and this has not even had a major attempt to prove this relationship.
Another, and I think a more appropriate track, is attempting to show a relationship between game learning (note this is not the same as game performance as this theory has generally been proven false) and the transfer of that learning to events outside the gaming experience. One fact that I have repeatedly observed that teams that forecast the relationship between simulation decisions and simulation results, have better results than those that do poorly when forecasting. But, what I have not observed is, students who forecast well while playing simulations also forecast well when they experience the need after leaving the business school experience.
Another methodology, but one I have not used, is the ability for game participants to graphically describe (and quantify) the relationships between the decision variables and the outcomes of those decisions in a simulation. I think this may prove to be very insightful to the learning process.
