gaming project. dynamic design

Gaming Project for San Francisco Tenderloin

The Gaming Project was submitted as an entry for the 2016 San Francisco Tenderloin Competition, by Mzo Tarr Architects. The aim was to provide a regeneration strategy for the Tenderloin district in San Francisco.

Mzo Tarr Team

Adam Tarr

Alexandru Senciuc

Peter Daniel Fazakas

Jelena Despotovic

Board 1: Game development
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Board 2: Game implementation
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The Gaming Project: How game theory can fight inner-city drug problems.

Tenderloin is a predominantly residential neighbourhood in down town San Francisco, California that suffers from social deprivation and high crime rates. The area’s architecture consists mainly of small, low cost units that over the years have attracted many isolated individuals. Immigration in the 1970’s led to families of refugees moving to the area, confined to the same cramped accommodation.

Problem:
In cities across the US, public officials and citizens are grappling with persistent problems such as drug abuse and drug-related violence. Communities have no dearth of good ideas about how to address these problems but institutional obstacles have made the implementation of their ideas, for the most part, unsuccessful. Tenderloin is no different to many other US cities in this respect. Attempts to improve the area through education, healthcare and providing employment opportunities have all failed.

Tenderloin’s drug epidemic poses a threat to the success of any proposals to improve the area. This drug epidemic and its impact on attempts to resolve Tenderloin’s problems is the focus of our project.

One difficulty policy makers have at present is that there are few (if any) technologies that enable them to explore and analyse the effectiveness of the proposals put forward to solve problems such as those that exist in Tenderloin.

Solution:
Our team used modern technology to create a computer forecasting system specifically for Tenderloin. This system is able to quantify both the financial and social impact of proposed drug prevention, treatment and intervention policies.

We believe that the application of ‘social policy gaming’ can help community leaders make better decisions when dealing with problems such as those that exist in Tenderloin. It can also assist with their training.

‘Social policy gaming’ is an exercise that allows individuals and groups to make social policy decisions in a realistic environment rather than in an abstract one. Gaming’s value lies in the fact that exploring problems when focusing on a specific scenario helps players understand the issues more completely, or at least gives them a different perspective.

How our computer platform applies social policy gaming: Our forecasting system is based on historical data for Tenderloin and the wider San Francisco area in relation to the following: crime, incarceration rates, policies, medical treatments, social and financial costs, and investment. It is able to analyse the impact of different proposals by extrapolating trends, correlations and patterns from this data.

What our computer platform does:
• Highlights the complex interrelationships between the various departments involved in decision making, from treatment to police, healthcare to courts;
• provides policy makers with a process and methodology to generate, analyse and deliver a series of quantified architectural and legislative interventions;
• subsequently enables the testing of different urban schemes and combinations over a variety of timelines, producing forecasts up to three years.

How we used our platform to generate urban interventions:
To test the platform, a workshop ‘game’ was set up over a one-week period with different roles adopted by three team members for a period of three rounds.

The rounds consisted of each player making architectural, social, urban or legislative suggestions relating only to the department area allocated to them (i.e. police (player 1), medical treatment (player 2) and courts (player 3)). At the end of each round, the controller (player 4) used the program to analyse the suggestions and issued the results to the players. These results informed the next round of suggestions.

At the end of the final round, the most effective suggestions were presented and integrated into Tenderloin, generating a three-year masterplan for the area. This program has showed how it was possible to control costs, maximise the success of medical treatment and reduce drug-related crime.

Some of our final interventions for the regeneration of Tenderloin include:
– Treatment centres: refurbished properties as part of the court appointed apprentice scheme, located less than 4 miles apart to reduce treatment drop-out rates;
– An online system that manages community interactions, monitoring crime and drug use;
– Decriminalisation of recreational use of marijuana;
– Refurbished buildings for housing;
– Facilities to purchase and consume marijuana;
– Reduced sentences for continued clean drug tests whilst incarcerated;
– Post incarceration: apprentice jobs, sponsors, expungement of criminal records, tagging and monitoring;
– Community events, fundraisers, community jobs;
– Police prevention actions: anonymous information collection system, distribution of free needles and drug kits;
– Rebuilding of housing and public spaces, environmental infrastructure, maintenance works as part of apprentice style court schemes;
– Raised levels provide views & points for community self-governance and monitoring.

Conclusion & Results:
The art of policymaking and realistic regeneration for an area such as Tenderloin is a case of continuously balancing tradeoffs. For example, in a bid to reduce the number & cost of incarcerations, the courts (player 3) had to balance early release programs and community safety, otherwise prisons fail as a deterrent.

The game enabled us to try “what if” scenarios. By playing the game, we were able to avoid policies and interventions which were simply attractive on the surface but flawed. Important lessons learned for drug policy was that prevention, treatment, police and the courts all have different foci in terms of space, time, funding and people, which lead to different objectives and priorities.

Observations during the game about the disconnect between the departments, encouraged collaboration between the players during round two leading to the successful targeting of re-employment for ex-offenders. Driven by opportunities created through the court appointed apprenticeship schemes; expunging of criminal records and the inclusion of the support networks within payoffs. As a result, year three saw a 17% rise in revenue helping improve the drug treatment programs, subsequently reducing incarceration levels and recidivism. Whilst increased employment and associated taxes, as well as revenue from the sale of Marijuana funded other community projects. Drug treatment costs were reduced by 24% compared to 3 years earlier whilst proposals saw a 55% reduction in re-offences and a 42% reduction in the total cost of incarceration.

Physical model