NetLogo User Community Models
WHAT IS IT?
The Exploratron versus Exploitation Dilemma in Innovation
This Netlogo example is meant to accompany Garica (200x), "Uses of Agent-Based Modeling in Innovation/New Product Development Research", Jouranl of Product Innovation Management. This NetLogo Exploration/Exploitation agent-based model has been designed for the observation of how different resource allocation strategies can affect performance and market share in differing consumer markets and competitive environments. It has been proposed that the ecology of competition influences the degree of emphasis of firms on exploration and exploitation activities for producing new products - the greater the competition, the greater the need to exphasize exploration of new technologies (Brenner and Tushman 2003; Ghemawat and Costa 1993).
This model looks at the product development strategy made by innovative firms as they create new products for the marketplace. The makeup of the consumers (early adopters of new technologies vs. late adopters of new technologies) and the number of competitors selling similar products to these consumers will affect the firm's new product development strategies and their subdsequent successes in the marketplace.
See Garcia, Rummel & Calantone "The Exploratron versus Exploitation Dilemma in Innovation: A Complex Adaptive Systems Approach", working paper at http://igimresearch.cba.neu.edu/netlogo for details.
HOW IT WORKS
CONSUMERS - Consumers either are early adopters (red) or late adopters (blue). Early adopters will only purchase 'innovative research' products and late adopters will only purchase 'incremental development' products.
Each turn (buying period) consumers randomly select two firms. Based on their buying preference (early adopter or late adopter) consumers buy from the firm which (a) has inventory of their preferred product and (b) has highest market share.
MANUFACTURER - In this model manufacturers compete for consumers by ‘manufacturing’ and ‘selling’ two different types of products, innovative and incremental. The goal of each manufacturer is to determine an innovation strategy, which is the percentage of resources allocated to exploration (innovative) activities and to exploitation (incremental) activities in order to optimize performance.
Before each buying period manufacturers follow one rule (see accompanying article for details) - it (re)allocates funds to exploration or exploitation activites to produce new products based on customer demand. When 'selling' products in the marketspace, if there is a shortage in inventory for innovative products (not enough to cover customer purchase requests) the firm allocates more of its resources to more exploration activities; if it has a shortage of products in meeting customer demand from late adopters it does more exploitation; if there is no shortage, the manufacturer doesn't change its mix of resources. If it has product shortages for both types of customers it looks to see which customers had greater demand and allocates resources to this type of activity.
CONTROL MANUFACTURER - A control manufacturer chooses either an exploration or exploitation strategy and keeps that strategy despite the make up of the customers in the marketplace. Use the control factory to determine which strategies work best in different types of competitive markets places.
HOW TO USE IT
‘INITIALIZE’: sets up the model. Clicking on the ‘initialize’ button creates the chosen number of Manufacturer and Consumer agents and randomly places their icons on the agent map. This button also assigns certain characteristics to each of the agents as will be described below.
‘RUN’: starts/stops the model. Clicking the ‘run’ button starts the free running processes of building and buying products. Clicking the button a second time interrupts the process. Any particular run can be started and stopped any number of times but the model can only be initialized when the model is stopped.
‘Command Center’ window: shows the iteration count. The ‘quarter’ was arbitrarily chosen as the unit of cycle time and thus the Command Center window displays: “Quarter xx” where xx is the current cycle count. When the model is initialized, the cycle count starts at ‘Quarter: 0’.
There are two settings for consumers:
There are three settings for manufacturers:
CONTROL MANUFACTURER SETTING
There is one settings for the Control Manufacturer:
The control factory is an agent which does NOT change its allocation strategy throughout the iterations. Its %-research is constant. This allows the user to determine if a particular strategy, for example an all research strategy or 100% initial research, is a winning strategy compared to other strategies.
Three charts along the right side of the model’s user interface show the progress of three important sets of data:
Each chart is a bar type and shows data for all manufacturers present. The vertical scales are normalized so as to optimize viewing resolution. There are always at least two manufacturer bars and can be up to ten as previously described by the initial-manufacturer-count setting. The Control Manufacturer is always present and is represented by the furthest bar to the left, which is also colored blue. All other Manufacturer bars are black.
a. Market share
b. Relative performance
c. Manufacturer % Research
When the switch is off, the research-risk setting is zero.
THINGS TO NOTICE
Run the model with the default settings. Watch how the market share changes for companies taking either a "all research" or "all development" strategy. Which strategy wins in the default mode?
THINGS TO TRY
Change the number of consumers, does this change the overall results? Change the % of early adopters, does this change the overall results? Is there a 'tipping point' when it makes sense for a firm to change its strategy?
Change the number of firms competing in the marketspace. Does this change the overall results?
Look at a 'Control Manufacturer'. Under what conditions can the Control Manufacturer beat others using an "all exploration strategy"? Using an "all exploitation strategy"? What can be learned about ecological competition?
EXTENDING THE MODEL
One way of extending the model is to add 'contingencies' using multiple switches. What will happen if consumer choice for buying from a manufacturer is related to price, marketshare, and product innovativeness instead of just marketshare alone? How is the model affected if only 50% of slack resources are spent on exploration and exploitation activities. Switches can be added to make these types of environmental changes. See an 'extended model' at http://igimresearch.cba.neu.edu/netlogo.
This model makes use of the slider and switch functions of Netlogo.
BehaviorSpace is a tool that allows one to perform experiments with the programmed model. (For more information on how to use BehaviorSpace refer to the Tool menu of any Netlogo model). This feature automatically runs a model many times, systematically varying the model's settings over a designated range of values. BehaviorSpace automatically varies the slider and switch ranges/settings to run 'experiments'. Simulations are run repeatedly where the tool varies the parameters and switch settings in order to observe the effects of the model’s behavior to these changes. This process of varying settings is called ‘parameter sweeping’ or ‘sensitivity analysis’. It allows exploration of the model's "space" of possible behaviors, and helps determine which combinations of settings or types of configurations under which the model best performs in multi-dimension parameter space defined. For this model, the ‘BehaviorSpace’ feature of NetLogo allows the user to map performance and market share in the space modeled by the control sliders and switches. The results of each simulation run are recorded in an Excel file allowing for future statistical analysis. This feature coupled with any statistical package provides for a potentially extremely effective modeling technique for this complex issue. (Netlogo Help Menu 2004)
CREDITS AND REFERENCES
This Netlogo example is meant to accompany Garica (200x), "Uses of Agent-Based Modeling in Innovation/New Product Development Research", Jouranl of Product Innovation Management.
To refer to this model in academic publications please use: Garcia, Rosanna and Paul Rummel(2004) Netlogo, Exploratron/Exploitation Dilemma in Innovation Model,
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