DevGame: Crowdsourced, developer-selected video game content

Crowdsourced, developer-selected video game content


Companies who develop and publish video games require a continuous source of unique and compelling creative content in the form of new game concepts, new game franchises, new features, and expansions for existing games to remain competitive in the global video game industry.  Given that firm resources are, by nature, limited in scope and scale, the firm’s organic creative resources and development capabilities will always be unable to globally maximize the scope and scale of the firm’s creative ideation. However, simultaneously, there is some level of latent creative ideation resident within the “crowd” of the online gaming community, where a diverse set of creatively-inclined individuals may be interested in providing creative ideas for these purposes in return for some reward.  Yet this “crowd” has neither the programming, development, and distribution resources of a video game company, nor the resolve and inclination of a commercial entity – both of these elements are required to move creative ideas from raw ideation to commercialized content.

Significant value could be derived from a platform that, on one side, provided a low cost means for these companies to acquire curated, crowdsourced creative ideas, and on the other side, provided a potentially rewarding yet near zero cost means for individuals in the “crowd” to supply those creative ideas.


Create a online platform marketplace where players are able to upload ideas for new content.  More specifically, users would have free access to the platform and a list of games by title with the corresponding features on which to submit creative content (e.g. maps, worlds, characters, weapons, storylines, etc).  The user would submit a written proposal with supporting graphic designs; if the user required graphic design support, DevGame would use in-house graphic artists to help generate a minimal amount of design content for a low nominal fee.  Next, DevGame would validate proposals as authentic and complete to ensure quality control and curation. With a sufficient number of proposals, DevGame would make individual content available to game developers, as well as central creative themes from the aggregated ideas.  Game developers would retain the role of selecting the specific content, compensating the selected user for his/her content. Although the marketplace would be initially used for product extension (e.g. in-game add-ins), the marketplace would eventually be expanded to allow for product derivatives or entirely new games.  In the latter case, game developers could use individual submissions or mix and match components from different entries. DevGame would work with both users and developers to assist in naming and licensing requirements to protect generated content.

Design of Demonstration

The demonstration would have to accomplish the two goals of validating the quantity and quality of submissions in order to make the marketplace viable for game developers. In order to achieve this outcome, DevGame would select a popular game title that depends on constantly producing new content in order to maintain its user base (e.g. Fortnite) and list specific features on which users could submit proposals. After an initial trial period of collecting submissions, the game developer would select an idea for implementation. The game would then be updated using the user generated content. Then over a period of several weeks, statistics about player growth, retention and reviews of new content would be collected and then compared against the historic figures. If successful, the results would show that the crowdsourced content would be more in-tune with what users want and would lead to higher participation.

Pilot Program

The pilot program would begin by launching the marketplace across a few specific college campuses that are well-known for producing graduates headed to video game development. We would partner with a well-known developer to ensure that users had access to a viable product, with the first contest beginning with in-game add-ons for the selected title. After a set period of time, the contest would close and the winning submission made public to the online forum, generating the necessary user interest for follow-on contests.

Commercial Viability

The global video game industry is projected to reach $90B in sales by 2020. Activision Blizzard, one of the biggest game developers, had $4.7B in revenue in 2017. Thus, enabling a 1-10% increase in either market size (value creation) or individual company revenue (value capture) represents both an achievable and substantial outcome. As a brief example of the size of development costs, Rockstar Games spent $265 million on the development of Grand Theft Auto V alone and has released several expansions since in order to maintain revenue. Although numerous companies have attempted to crowd-select creative content (i.e. have the crowd decide the next feature of the game), no companies currently act as a marketplace for the aggregation of creative content. Moreover, crowdsourced and selected games, while numerous in quantity, have achieved fairly mundane results. Thus, DevGame would be a free service for the user while charging a fee to game developers to access crowdsourced creative content. Additionally, companies would provide incentives, either monetary or in-game rewards, to players whose content was selected and then developed.  Following the completion of an initial launch phase, DevGame could increase its value capture by implementing some scalable toll or fee on the value of content passing in both directions across the DevGame platform – from company to user, and from user to company.


Activision Blizzard Inc Form 10-K

Ubisoft Earnings

Global Video Games Market from 2011 to 2020

Crowdsourced Video Games are a Terrible Idea

5 of the Best Colleges for Gamers

Team Members

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer

Pitch: CarWolf

Automated Agreements: CarWolf


Numerous parties could benefit from a system whereby complete car operating and service history for the entire auto market is maintained in a secure, verifiable, permission-ledger system. Insurance providers would be able to characterize risk more accurately for an individual driver by not only collecting data on how the driver uses the car (e.g. Progressive Snapshot), but also by having a complete picture on the “health” of the car, regardless of whether the car was new or used. While CarFax attempts to compile some of this information, it is extremely limited in scope. In addition to insurance providers, car manufacturers, with an accurate picture of a car’s usage, would be able to schedule tailored service, as opposed to following a set maintenance interval, regardless of whether the car was new, used, or “certified-new.” Over time, the manufacturers would be able to develop warranties that are more in line with actual needs.

While the above discussion highlights the direct benefits of such a system, there is another obvious indirect benefit from such a system: the complete historical record of all car operating and service information would solve the problem associated with asymmetric information in the “market for lemons.” Neither buyer nor seller would have a one-sided information advantage, leading to the creation of value by more closely matching demand with supply, and price with value.


Our solution is to employ blockchain technology in a permissioned-ledger system to create an immutable record of every car’s history by attaching operating and service data to the vehicle identification number (VIN), thereby providing car manufacturers with actionable car operating data and solving asymmetric information problems on behalf of both insurance companies and consumers. The CarWolf system consists of two key components: the actual distributed-ledger system and a “black box” collecting car operating information.

While the CarWolf distributed-ledger system would compile all car information via blockchain technology and be permissioned to protect all parties involved, the “black box” would plug into the service port in a car and access car operating information, similar to Progressive’s Snapshot. However, unlike Snapshot, Wolfshot ™ would collect the information and periodically upload it to the blockchain via a mobile app, providing an immutable record of the car operating data. Unlike Progressive Snapshot, the CarWolf system would also require maintenance providers to upload service performed, whether routine or unscheduled, to the blockchain in order to attach a more complete picture of the car’s health. Maintenance providers would be incentivized to upload information either based on being the service department in a dealership (therefore doing so on behalf of the car manufacturer) or by being in the “provider network” for a particular insurance company. The insurance company would benefit from having a more complete picture of a car’s history from which to assign risk and value the car, and the maintenance provider would benefit from being the recipient of more business. Finally, title information would get uploaded to give a complete picture of car ownership.

Design of demonstration

To demonstrate the value of the business, we would want to highlight three key points: first, that system can collect complete car operating data; second, that the available information would vastly exceed currently available sources to solve information challenges for manufacturers, insurance providers, and car buyers; lastly, that the system would be able to handle the quantity of information provided in an immutable ledger. Therefore, the demonstration would use a car with Wolfshot installed and compile information over a 6-month period. Concurrently, the car owner would provide service records over the same period of time to provide a point of comparison of information available under current mechanisms in the most optimal use case. The car and current documentation would then be made available to a set of manufacturers, insurance providers, used car dealers, and prospective buyers, whereby all parties would be asked to estimate the particular metric for their given interests (e.g. the insurance provider estimates monthly premiums while the used car dealer estimates the resale price). Finally, the complete CarWolf data would be made available to another set of the above parties performing the same tasks. The difference between the estimates would quantify the benefit of CarWolf for the various applications.

Pilot Program

Given that the success of this program is dependant on having a large amount of cars in the network and participation from both insurance companies and auto shops, the pilot program would likely begin by partnering with an insurance provider to kick start the program. The insurance companies would offer customers a discount on their policy for using Wolfshot in their car. Wolfshot would not only capture information about how the car was driven, but also record information reflective of the overall health of the car, such as how frequently the oil is changed, wear on the brakes, accident history and all work done on the car. This information would then be stored on the CarWolf blockchain. The program would then track how much the car was resold for over time and the adjustments in users insurance policies based off of the information from Wolfshot. By the end of the pilot we would ideally have collected enough data to show that there were significant savings from the more accurate resale values and adjusted insurance rates to expand to other insurance companies.

Commercial Viability

Given the size of the used car market (~40 million cars sold annually) and the extent to which comparable businesses presently thrive, it is clear that there is a demand for vehicle information to inform better buying and insuring decisions.  Since several businesses have demonstrated success by attempting to address this concern, it appears likely that an incorruptible, immutable, and more complete vehicle history has value in the market. The biggest barrier to commercial viability is driving adoption of the Wolfshot device.  Without new data, such as that automatically generated by the Wolfshot device, the business is little more than Carfax made faster, more secure, and more efficient through the use of blockchain. Vehicle manufacturers who fare well in reliability rankings would stand to enhance their favorable positions via the longevity, value-retention, and reliability data CarWolf would provide, while vehicle manufacturers who tend to fare poorly in reliability rankings would be incentivized to increase their performance in these metrics as they face higher degrees of transparency and accountability in the market.  Privacy-conscious car owners may hesitate to feed car and driving information to a third party without a proper incentive and measures to mitigate their privacy concerns. Therefore, the success of the venture depends, at least in part, on broad adoption of the Wolfshot device. The design of the Wolfshot device, and that design’s ability to mitigate privacy and trust concerns from the consumer, are critical to this broad adoption. One component of the value proposition to the car-owner is the prospect of selling their vehicle for a higher price in the used car market. Further incentives would involve reduced rates with insurance providers and at local partner garages in the CarWolf network.



Used Vehicle Sales Look Set to Hit All-time High in 2016

Progressive Snapshot


Edmunds 2017 Used Car Report

Automotive OEM Warranty Report

Team Members:

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer



The US lunch industry is in a state of flux with an ever-increasing number of businesses opting for in-office options vice eating in a restaurant to either increase productivity or provide additional work benefits. Traditionally, many companies have spent large amounts of money on office catering, which largely goes to waste.  Thus many firms are turning to technology to answer the question, “what should we have for lunch?” As such, firms now specialize in delivering individualized, restaurant-quality food directly to the workplace. These firms take advantage of the many restaurants offering a wide variety of food near urban offices. However, ordering lunch for a large group of coworkers can be a time consuming task, particularly when attempting to ensure everyone gets what they want.  Forkable seeks to bridge all of these elements and use machine learning and technology to match workers to restaurants (food) and vice versa.

Summary of Solution

Forkable uses customer inputted preferences, meal feedback ratings, and individually indicated tastes along with machine learning algorithms to learn the food preferences of office workers and then automate the food ordering and delivery process for individually satisfying office lunches.  This approach reduces the complexity of ordering lunch for large office co-worker groups by not only predicting what restaurant and food every person in the group will enjoy, but also completing the ordering, pickup, and delivery of those lunches. Forkable also allows for users to meet budget constraints and use human override if required. Additionally, Forkable eliminates the waste associated with catering corporate lunch as well as cutting down on the internal administrative cost of ordering lunch, saving companies money.

Effectiveness and Commercial Premise

Forkable is part of the emerging space that is the intersection of the $9B catering and $200B U.S. lunch markets. In this area, firms typically charge a delivery fee plus an order surcharge to generate revenue. As Forkable is still very much in the startup phase, it remains impossible to directly measure either the potential or effectiveness of Forkable. However, Fooda may provide a closer benchmark by delivering meals from a single restaurant, earning $48M in revenue in 2016. At the top end, Grubhub provides a measure of a large, public company that allows one to achieve the same results with more effort required from the user, generating $683M in revenue in 2017.

Competitive Landscape

Lunch catering is a crowded and diverse competitive landscape.  There are low barriers to entry and a wide variety of approaches to catering.  There are traditional businesses that have a limited tech presence, but will cook, deliver, and serve the food.  There are also a variety of well known tech-enabled businesses, like Grubhub, Postmates, and Uber, which focus just on the delivery of the food from restaurants to customers.  Other lesser known office-catering businesses include Fooda and Eat Club. Fooda’s business model involves bringing local restaurants into offices to serve a selection of food directly to the customers.  This model is interesting and can operate with low overhead, but a narrow selection from a given restaurant will not necessarily have something that everyone likes. Eat Club has a model that is similar to Forkable in that it allows customers to individually select their preferences.  In this way, it is similar to just ordering from a menu. It does not use machine learning to select dishes and restaurants which are best suited to customer preferences in the way that Forkable does.

Proposed Alterations to Increase Value

Forkable currently optimizes meal selection for the customer through the previously described preference-rating feedback loop. However, restaurant selection occurs by using websites known for ratings, such as Zagat, Yelp, and Foursquare, and selecting 10 restaurants in a given area from which to offer meals. As a result, the process could be improved by using machine learning to optimize restaurant selection. Forkable would take the upcoming week’s customers (which they know in advance) and identify the restaurants through machine learning within a predefined radius that provides the highest number of potential matches for the customer set. This process would accomplish two goals. First, it would increase the quality of the service and customer satisfaction as a result. Second, it would decrease costs through both restaurant search-radius optimization (i.e., pick an adaptable area that will minimize delivery costs) and decreasing the cost of customer acquisition/retention. By offering a higher quality service at lower cost, Forkable would increase its customer base, attract more restaurants, and be able to offer more options, all part of the virtuous flywheel associated with indirect network effects. As a result, this improvement would both create value as the emerging market size increases, as well as capture value by removing other close competitors such as Fooda and ZeroCater.




Forkable Wants Companies to Forgo Buffet Style Lunches

Grubhub 10-K

Food Tech Startup Marries In-office Catering with Recurring Revenue Model

Team Members

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer