Santa Fe Rfei Data Platform Text Only (F5b21ea4)

**Artifact** from Bead: F5b21ea4 · [canonical source](https://redfish.acequia.io/guerin/.agents/f5b21ea4-2b73-4c8b-96f0-892f63ad86cf/2026-06-04/artifacts/santa-fe-rfei-data-platform-text-only.md) · session 2026-06-04 · discussion: Talk: F5b21ea4

![][image1] Project Name: **Santa Fe Data Platform Request for Expressions of Interest (RFEI)** Lead Organization: **Redfish Group LLC** 1600 Lena St. Suite D1 Santa Fe, New Mexico, 87505 (505) 577-5828 stephen@redfish.com Data Submitted: http://bit.ly/RedfishGroup\_CityOfSantaFeDataPlatform Collaborators: **Santa Fe Project and Venice Project Center: Worcester Polytechnic Institute** Leader**: Dr. Fabio Carrera**, PhD MIT Department of Urban Studies and Planning Advisors : **Dr. Tom Johnson,** Institute for Analytic Journalism **Dr. George Duncan,** Data Privacy Expert, Carnegie Mellon **INTRODUCTION and A PROPOSED VISION** We applaud the committee and the organizations volunteering valuable thought-cycles and funding to selflessly serve Santa Fe. Knowing many of you, we share a common passion for Santa Fe. We recognize that this RFP was for building a City Data Platform and the committee has a Data Portal/Dashboard in mind. We appreciate that the scope was open-ended enough to allow for many creative voices and that it recommended a “walk before it can run” test case. At certain times in history, change in paradigms is concentrated in certain cities of the world. Florence, Berlin, Prague, New York, Hangzhou, and Paris are examples. We believe Santa Fe is one of those cities where different thoughts are coalescing into new paradigms of governance of information and action. Santa Fe is offering the world critically new ways of thinking about the nature of intelligence, living systems, the function of cities, design of complex systems, artistic expression, new economic forms and even new approaches to spirituality addressing our place in the universe. Our proposal here reflects thinking and development our team has done over the last 20 years in complex systems modeling and what a system architecture might be to satisfy a “run case” of an extreme technical challenge. Our proposal will address the “walk” case of a City Dashboard, but we hope to broad brush in the committee’s minds what a “run case” could look like and what an associated “data platform” might be to support it. Our technical approach for the “City Different” is an “Information System Different”. Or, What does it mean for a city to exhibit collective intelligence? Thinking more broadly than just providing a data platform, we ask what does this mean for a city to be intelligent? Or stated another way in terms of ecological cognitive science \- a "living city" capable of perception and action. We believe one of the major design goals for a city is to enable citizens to be collectively intelligent. With an emphasis on decentralization, we move from government *collecting intelligence* to governance systems for *collective intelligence*. We believe that stakeholders should be empowered by their data and is best enforced by enabling users to effectively share information their data has to provide. To provide agent-mediation over data, we do not want to centralize data sources which may compromise certain departments/organizations. We want to provide a space where users can come together, combine their data with others, and produce new insights into problems they are trying to solve. Our design is informed by the scientific explorations of the Santa Fe Institute and Complex Adaptive Systems for which our City has gained an international reputation and associated expectations to apply this kind of thinking. This research is fundamentally informed by how nature achieves cognition from simpler decentralized components. This research could be summarized with the question: “How do physical and biological systems become collectively intelligent without the use of centralized nodes?”. For a refresher example for folks that may not have been exposed to much of this Santa Fe research, please see the appendix for an example model. Realtime.Earth is our web-based platform for real-time collective intelligence and GIS collaboration enabled by imagery capture and collection, data distribution and model visualization. The imagery and GIS data, enabled by photogrammetry, produce insights by augmenting images with map information and producing data layers from imagery. This combination of image and GIS data has the ability to produce actionable data visualizations. Realtime.Earth can leverage a decentralized infrastructure \- the key is to utilize peer-to-peer networks, connecting mobile phones and laptops, and leveraging their combined computational resources. Realtime.Earth runs in a browser; therefore, it requires no pre-installation of software, applications, or reconfiguration. The operating philosophy is similar to Uber, and Airbnb. Both companies own little assets \- their value comes from the coordination; Realtime.Earth takes advantage of existing assets (e.g., cellular phones, cameras, satellites) to provide situational awareness and analytical insights through the fusion of ground, air, and citizen intelligence into one geo-rectified scene. There is no additional infrastructure to install \- it’s already in the community’s pockets as phones. What is missing is real-time coordination. After a “walk case” of tourism, for which the City only has an indirect influence, we propose a thought exercise about a domain more within the direct responsibility of the City. Consider this partial list of City Services: ![][image2] Let’s consider a “run” case for which we’re proposing a supporting decentralized architecture for two city services under the direct control of the City \- Police and Fire. Last year, on Nov 8, The town of Paradise was destroyed within 4 hours from the start of the Camp Fire with 85 people dead within the first 3 hours. 18,804 structures were lost. The citizens had no notification, no realtime shared maps, no idea where the fire was or which evacuation routes were open \- many died in their cars stuck in traffic dead-ending into the fire. While this information was individually known by the citizens and photographed on their phones, they were unable to coordinate from the bottom up. Instead, they relied on authorities to observe, map, notify and inform citizens what to do. This is a failed strategy with massively tragic results. There’s no way intelligence can be collected, centralized, processed and disseminated to a local decision-maker in that amount of time \- regardless there are not enough available official resources to rescue the stranded. Official resources were barely on-scene in the first 8 hours of the incident. ![][image3] CAL FIRE Director Ken Pimlott reviewing a progression of the Camp Fire with Bill Wittaker of ABC’s 60 Minutes using Simtable™. [Play 60 Minutes video interview here](http://bit.ly/60MinutesSimtable) ![][image4] Simtable, as a member of Cal Fire’s Investigative Unit, constructed the official reconstruction of fire from citizen photos here: CampFireDayOne Progression: [http://bit.ly/CampFireDayOne](http://bit.ly/CampFireDayOne) The “run case” we ask you to consider is the scenario where a fire starts up near the State Park on Hyde Park Road with raging winds, billowing smoke plumes and scattering firebrand embers spotting fires miles downwind. Could the city’s data platform help us be any more prepared than Paradise was? How could a realtime “City Data Platform” help the citizens organize? It could give relevant and specific information to different citizens on different evacuation options. It could help citizens account for and pick up neighbors that couldn’t drive themselves. And it might help minimize cars coming down limited evacuation routes on Hyde Park Road, Gonzales and across the valley in Wilderness Gate out past Santa Fe Prep. We advocate that Santa Fe offers the world a way of moving from the centralized mindset of a government system “collecting intelligence” to a paradigm of “collective intelligence” that is coordinated by the “governance” platform. The architecture we’re proposing could handle millions of mobile videos and videos streaming live and calibrated in realtime to generate a 3D scene of the unfolding event while keeping private areas unshared. This would be nearly impossible to guarantee if the media went to centralized servers first before processing. Simtable was recently invited to the US Senate to demonstrate RealtimeEarth to describe how citizen and first responder imagery can be fused in realtime in a decentralized platform. Please watch the video below. We will provide links to several videos and applications throughout the proposal. We ask that you explore them as they convey our architecture visually \- it is a challenge to describe in text: [![][image5]](http://bit.ly/SimtableSenateSFRFP) Simtable’s testimony to US Senate Energy and Natural Resources Committee describing RealtimeEarth as a response to the lessons learned on the CampFire: [Play Video](http://bit.ly/GuerinSenate) [![][image6]](http://bit.ly/MariaFireRealtimeEarth) Example of using RealtimeEarth to integrate social media imagery and oblique cameras during the first night of Ventura, California Maria Fire Oct 31, 2019\. The calibrated imagery is used to update our fire progression model to inform citizens and responders. [Play Video Here](https://bit.ly/RTEMariaFire) For the “walk case” we are proposing to make a web application that is an agent-based model of tourists, residents, and other occupants of the city. The model would be city or county-wide and the platform will support additional layers of models as developed. An example of an agent-based model is this Zozobra model below that we did in 2004 to study evacuation behavior. **![][image7]** Redfish Zozobra Evacuation agent-based model that predicted excess stress on center bridge in 2004 which actually had a buckling event in 2014\. At this year's Zozobra, we deployed RealtimeEarth in the Emergency Operations Center at SFUAD and georectified 12 Police Camera systems in addition to mobile phone’s streaming video georectified live on the field during the event. One small but critical event that occurred this year was the misrouting of buses as they returned from the first busload to the South Capitol Railrunner station. RealtimeEarth was successfully used to mitigate this error in routing. You can see this a video of this happening below: [![][image8]](http://bit.ly/ZozobraBusses) Listen in as Santa Fe Police, Santa Fe Emergency Manager, David Silver and Santa Fe Trails solve the problem in realtime of Santa Fe buses getting misrouted up Canyon Road on their return to pick up waiting passengers at 2019 Zozobra. [Play video here](http://bit.ly/ZozobraBusses) As successful as that mitigation was, we think we can do better. The cameras that were georectified during the event could have been used to select the waiting passengers in the imagery and send them a message of the delay in the buses, and a map on their arrival time. The innovation we’re proposing is that the attendees would not have to share their location with the government but still could be notified through a software agent that works on their behalf and knows their location. ![][image9] Realtime image of passengers waiting for misrouted busses at 2019 Zozobra. The cameras are calibrated by RealtimeEarth so that the latitude and longitude of each pixel is known. It is possible in a future Santa Fe Data platform to notify those people that the bus is on the way without those people revealing their location to a centralized cloud which would allow citizens to maintain their privacy while getting intelligence. **TECHNICAL QUESTIONS** **1\) What technical approaches would you recommend and/or deploy to establish a platform, to be owned and updated by the City of Santa Fe, to house multiple types of data? Include in your explanation how you would house data for the initial two questions while also planning ahead for the expectation of additional data sets in the future.** We recommend a novel approach. Don’t house the data on centralized servers. Make it possible for all government employees, departments and citizens to house their own data and applications and coordinate intelligence between them. This can be done with only web browsers coordinating in what we call the "Acequia" which we believe has the potential to provide an alternative to the cloud. We think there is an opportunity for a “moon shot” that goes beyond a centralized data portal. We believe Santa Fe can be a world model city for deploying an information system that enables the citizens and its government to be collectively intelligent in realtime. Further, work on this project will help our local company develop our technology for use in the rest of the world. Realtime.Earth is a decentralized browser-based platform for authoring and deploying web apps where data is stored locally and privately in browsers and avoids storing data on centralized servers. Our approach requires neither servers hosted on-site nor hosted in the cloud for data storage, source code hosting or computation. Yet, citizens and government departments can experience a realtime, dynamic website while their private data remains secure and useful. We believe Realtime.Earth is a radical and novel approach that has the potential to change real-time geospatial computing and web application development in general. It embodies the very essence of “Santa Fe Thinking” for the design and engineering of self-organizing systems that achieves collective intelligence while maximizing privacy and collective action while minimizing the constraint of action on the individuals. Data is encrypted, made redundant and accessible to the community on a federated decentralized network of browsers and extensions running in citizen and city employee browsers. All compute happens in the browser and requires no server-side code execution nor centralized data storage. Browsers, in effect, become servers. Realtime data is mediated through software agents running in browsers with agents communicating in a peer-to-peer mode via encrypted connections without sending data through traditional “backend” servers. We are basically inverting the paradigm in computing where we put the web clients at the core and traditional servers (if used at all) are at the edge. Restated, a network of browsers is the full compute stack \- we call this the Acequia™. The backend goes away. Our platform approach is called Realtime.Earth™ which has four components: * Acequia \- decentralized encrypted data storage and distributed state management * Agentscript \- our open-source scripting language designed for agent-based modeling * AnySurface \- projected augmented reality that makes every surface in a room interactive. * LiveTexture \- realtime reality capture from cameras [![][image10]](http://bit.ly/AnyHazardWildfire_SantaFe) | Recorded demonstration of agent-based model of wildfire Scenario up Hyde Park Road with traffic evacuation running in RealtimeEarth / AnyHazard. Simulation completely within the browser with no server-side code. Demonstrates real-time streaming of GIS intelligence between browsers and realtime embedding on news sites and public alert system. [play video here](http://bit.ly/AnyHazardWildfire_SantaFe) | Interact directly with this model in your browser [by clicking here](http://bit.ly/AnyHazardClipboard_SantaFe) or by scanning this QR code on your phone. Apple iOS users can open their camera app and point at QR code below. Note you are becoming a node on a decentralized network via a browser with no installation of apps or code running on the server:![][image11] | | :---- | :---: | We also request an opportunity to present a live demonstration of Realtime.Earth to the committee as it is our experience that many get a firmer grasp of the transformational potential of this approach through actual use instead of textual description. **2\) How would you address the requirement that the project deploy a shared programming language, with accessible source code, so that future researchers can build on the system?** Agentscript is the core language in which apps are written on our platform. We developed Agentscript as an open-source scripting language for agent-based modeling in javascript for deployment in the browser. See Github: https://agentscript.org Agent-based modeling is a fundamental mathematical simulation approach to the study and engineering of complex systems. In the 90’s the SWARM agent-based modeling system, initially developed in Objective-C was developed at the Santa Fe Institute. AgentScript uses the same semantics as SWARM and the popular NetLogo framework but has been modernized into Javascript for running in browsers and can scale across many nodes simultaneously to run massive computations rivaling distributed high-performance computing on supercomputers. Further, Agentscript is designed to be deployed into real-world processes tied to Internet of Things (IoT) sensors and actuators. With this capability, we move from “models” of the world to actually running and interacting with the world. We describe this transition as moving from “agent-based modeling” to “agent-oriented programming”. In addition to Agentscript, Acequia supports REST endpoints for publishing and subscribing to and from external systems. Researchers and analysts can use more traditional tools like Python, R, C, Java, etc. The core datasets are Vector (geoJSON) and Raster data tiles (PNG and GeoTiff), 3D Tiles and pointclouds that should be familiar to most geospatial professionals. Collaborators can use their tools for offline analysis or set up automated scripts / cloud compute for realtime publishing and subscribing to assist in our realtime collective intelligence system. Ultimately, though, It is through the use of apps described next, that meaningful and useful information is collected. The core logic of these apps will be written in the browser in Agentscript or Javascript more generally. **3\) What data sources would you recommend in order to answer the first two questions?** For the last 8 years, teams of 24 students come each year to work on projects in Santa Fe and are supervised by Dr. Fabio Carrera and members of our team. Coincidentally, 4 years ago, a simple version of the “walk case” of creating an open data portal for Santa Fe that included an estimate of tourist and citizen populations was simultaneously done at the Santa Fe Complex and in Venice, Italy by student teams from Worcester Polytechnic Institute. ![][image12] 2015 Dashboard and open data portal. Tourism and Local Population Dashboard, Santa Fe Complex and WPI Venice Project Center. Note some “link rot” as Google Maps changed their free policy. [Live version here](http://dashboard.cityknowledge.net/#/vpcb15). The Santa Fe version was handed over to the city to host but was never maintained or updated. This supports our belief further that information systems should be decentralized to avoid reliance on a single centralized node of failure. The data sources then included AirBnB public API for bed occupancy counts, airport and train arrivals. Similar data sources could be found today to update the application. But to move beyond this to reach a “run’ capability that has direct interaction with the tourists and local populations is outlined next. The main data source will come from the development and deployment of useful apps on the Realtime.Earth platform. Let us stress this point as it is the core of our approach to a platform. The most progressive corporations have access to predictive data because they deploy useful apps that capture that data from users in realtime. For example, Google knows real-time traffic congestion and speeds because millions of users are using Google Maps to navigate. Google knows more about traffic and driver origins and destinations than the Department of Transportation does despite the fact that the DOT deploys millions of dollars of equipment to try to sense traffic. Ultimately, to understand tourist and resident behavior, the City needs a platform for developing and deploying useful apps. Example Apps that could be developed on the RealtimeEarth platform for tourist and population interaction include: 1. resident and tourist mobility app that would offer on-demand pickup by Santa Fe Pickup. We had previous project scoping and approval as Santa Fe Trails to experiment with more point to point on-demand service with one of the underutilized bus lines to help solve the “last mile” of public transportation but it stalled when it reached the city manager level 6 years ago. 2. Santa Fe “Real Coin” app that allows for payment of city services like parking meters, public transportation, parking garage, REC center, etc via a QR code as well as a peer to peer transactions at local companies, local peer rideshare, Indian/Spanish/Folk Art Market, etc. These are the norm in China using tools like WeChat’s WePay and Alibaba’s AliPay. The same coin can be earned by capturing on-demand drone, phone and security cam footage contributing CPU, storage, and bandwidth to increase the capacity of the RealtimeEarth network. 3. RealtimeEarth Telepresence / Pay-per-View reconstruction of a live event like a MeowWolf show, Zozobra, Fuegos, Conference or kids soccer game. 4. collective neighborhood security system where private security cameras help monitor a neighborhood without feeding raw video imagery to neighbors or directly to the police in the way Ring networks are designed. 5. Graffiti curation for alternative art walks for locals and tourists. A kind of pokemon/ingress experience where new graffiti is documented either for future appreciation or cleanup 6. Friend of a friend ride-hailing for local and airport trips. One could see how these example apps could generate information for realtime intelligence on citizen and tourist behavior. This live data will be used to calibrate a live continually-updating agent-based model of the full City of Santa Fe population running in the browser. Our main difference is making it easy for many participants to generate apps that generate meaningful data that can be aggregated for other uses with full user permission and assurances of privacy. The model can be extended to the County of Santa Fe if additional county departments and their data are made available. The model will be initialized with a synthetic agent population from micro individual datasets from city departments like emergency management alert notifications, water and waste management. Each synthetic citizen will be assigned a virtual software agent that will govern its data use. Note that that agent will be encrypted and neither accessible to us as developers nor stored on any central server. The agent will have access to personal data stored in the locations around the city where that data is generated and will govern its use on behalf and under the control of the citizen. Any information released for analysis or coordination will maintain the privacy of that citizen. Dr. George Duncan will be our key advisor, on best practices and methodologies of maximizing privacy when aggregating data to avoid possibilities of inference of identity when multiple datasets are composited. More macroscopic data like State Gross Receipts and Lodger Tax, AirBnB reservation counts via their API, traffic counts, school enrollments, census data, airport arrivals will be used to constrain summary statistic distributions. **4\) If any of those sources require purchasing/licensing, it is strongly preferred that the purchase or license be made in the name of the City of Santa Fe. If awarded, how would you address data ownership? Any challenges might you anticipate related to ownership?** Any external data licensed would be licensed to the City of Santa Fe. Any information shared by a citizen will remain owned by the citizen. Data generated by city departments and managed by department agents will remain owned by that department. RealtimeEarth is a platform for writing useful apps that citizens will use and where the city’s realtime information will come from. Note, the city will have access to useful information but necessarily, not the private source data of the users. The city and citizens will have the capability to write queries that will be transparent and inspectable so citizens can authorize and give permission for that data use. **DESIGN QUESTIONS** **5\. How would you address the goal of creating useful, clear visualizations of data to inform policymakers/the public? (Describe your general plan and provide visual samples)** Realtime.Earth Browser-Based Simulation Engine running on the website but also projected out into the room via AnySurface. Note that all the projected visualizations are simply running in browsers on the RealtimeEarth platform: ![][image13] Example video of customer-made video of citizens exploring real-time simulation in browser projected onto Simtable of Bushfire in Victoria, Australia. [Play Video here](http://bit.ly/SimtableVictoria) **6\. How would you address developing a website, to be operated by the City of Santa Fe, with a user-friendly interface to publish data and accommodate future expansion?** The “website” would be equivalent to running a realtime equivalent of a live “Google Earth” in the browser with agent-based models of the city of Santa Fe residents, tourists and other occupants. This will be equivalent in design to a realtime SimCity. Beyond simulation, realtime imagery from city and citizen owned cameras will be texture mapped on the model. Contrast this live approach to the more static nature of Google Earth which has imagery often a year old and static 3D models. We are proposing not only dynamic 3D texturing but realtime 3D models as well. The RealtimeEarth engine supports data import via drag and drop into the browser as well as the ability to export data. The interface would include an integrated development environment (IDE) to allow for user scripting of layers and apps in the browser. There is no need to script to external files and then upload to a server and refresh a page to see a new version of the application. **BUDGET** **7\. What would the project cost? Please submit a budget addressing the items above.** our proposed scope is: 1. deploy Acequia \- the decentralized web app platform 2. create and deploy agent-based model calibrated against tourism/population data 3. continuously update agent-based model against data feeds 4. deploy AgentScript engine to support thousands of different models and layers for visualizing and interacting with the domains of the city 5. create 2 reference Agentscript applications for tourists and citizens **$290,000** **We do not want price to be a barrier for the deployment of RealtimeEarth in our local city. This budget is negotiable based on the final scope and available funds.** We would grant a perpetual license to the pre-existing code-base of Realtime.Earth to the City of Santa Fe. City of Santa Fe would be responsible for Gross Receipts if applicable. ALTERNATIVE SCOPES AND BUDGET Lower alternative: 1. Deploy web-based Agentscript agent-based model and visualization 2. calibrate against committee supplied data from other collaborators: **$150,000** Highest conceivable scope budget for a full one-year project: 1. Includes work in our top proposal and extends to a “run” app beyond “walk”. 2. realtime Emergency response enabling citizens to self-organize to help in an event like the loss of Paradise during the 2018 Camp Fire. 3. Realtime maps included with emergency alerts and embedded on all communication channel websites to include City of Santa Fe Emergency Management, The New Mexican, Hutton Broadcasting. 4. The ability for 911 dispatch to request caller turn on phone camera and submit imagery 5. stitching together all available volunteered cameras in the city government and from citizens while maintaining privacy. **$864,000** **QUALIFICATIONS** **8\. Are you applying as an individual organization or a coalition?** Individual Organization, Resident Business Santa Fe Local company, RedfishGroup. We are willing and hopeful to work with other respondents to the RFEI. We anticipate our expertise would be on the decentralized platform design, agent-oriented app development and deployment, and some visualization and human-computer interaction. We would welcome other developers with novel datasets, app development ideas, cloud computing that could be used as input and data analytics. **9\. Please list qualifications of the organization. Include relevant organization experience, similar past projects, and whether past clients have included government entities. Also include educational backgrounds and professional experience for key team members.** **Stephen Guerin** \- CEO Redfish Group. Inventor Simtable, Faculty, Santa Fe Institute Complex Systems Summer School, Economics and Cognitive Science, Arizona State University **Joshua Thorp** \- CTO, RedfishGroup, Computer Science, Cornell **Owen Densmore** \- Author of Agentscript. Original design team of the first Apple Macintosh **Kasra Manavi**, Ph.D. \- Machine Learning, Computer Vision, University of New Mexico **Emma Gould** \- Senior Developer \- RealtimeEarth, Computer Science and Physics, Smith College Cody Smith \- Senior Developer \- AnyHazard, Computer Science, University of New Mexico Marcos Lopez \- Developer and User Support, Simtable ![][image14]Our team has deployed over 100 Simtables around the world ![][image15] **Advisors:** **Tom Johnson**, PhD, Executive Director, [Institute for Analytic Journalism](http://www.analyticjournalism.com/). A leading researcher and advocate of open government and datasets. **George Duncan**, PhD, One of the world's foremost experts in data privacy in public datasets. Professor Emeritus, Carnegie Mellon University. **Background on RedfishGroup** RedfishGroup LLC is based in Santa Fe, New Mexico, a global hub for the science of complex adaptive systems. Redfish is a world leader in agent-based modeling, data visualization, and human-computer interaction; over the last ten years, the company’s primary focus has been developing agent-based modeling frameworks for exploring and visualizing complex scenarios involving physical and social phenomena. In 2009, Redfish invented the Simtable, a digital interactive sandtable that visualizes agent-based models for the wildland fire, emergency management, defense, and academic communities. ![][image16] Time Magazine featured the Simtable as one of the top 5 inventions of the year along with Apple’s Siri and the Lytro Camera. The digital sandtables (Simtable™) are internationally distributed, with customers including the California Department of Forestry and Fire Protection (CAL FIRE) (Figure 1). These tables are used in a variety of applications including emergency response training, community outreach, communication to stakeholders and STEM education. Shortly after the events of the Yarnell Hill Fire and the death of 19 firefighters, Simtable began to research and develop a platform that could provide first responders, decision-makers and the public with situational awareness in real-time. The concept under development is known as Realtime.Earth; it uses imagery captured on from citizens, crews, drones (UAVs), satellites, and social media and fuses these images with GIS data into live 3D models. Our goal is to open the bandwidth of information between citizens and government organizations to enable more evidence-based decision making. We do this by combining imagery and GIS creating a virtual model of the scenario. We are focusing our efforts on leveraging the ever-increasing number of imagery and data sources and the ability to distribute information via map sharing. Realtime.Earth has been under development for five-years and being deployed in Wildfire and Emergency management around the world. Most importantly, its focus matches the project goals of the RFEI including the ability to produce actionable data visualization, evaluate accountability targets and metrics, and enable the community at large to answer their own questions. Realtime.Earth is back loaded with a commitment of time, money, experimentation, and resources—it is now ready for a major leap forward in a full community deployment. Let Santa Fe be a model to the world. REFERENCES Thorp, J., Guerin, S., Wimberly, F., Rossbach, M., Densmore, O., Agar, M., Roberts, D. (2006) Santa Fe On Fire: agent-based modeling of wildfire evacuation dynamics . in Proceedings of the Agent 2006 Conference on Social Agents: Results and Prospects, ANL/DIS-06-7, ISBN 0-9679168-7-9, Sallach, D.L., C.M. Macal, and M.J. North (editors), co-sponsored by Argonne National Laboratory and The University of Chicago, September 21-23. Joyce, D., Kennison, J., Densmore, O., Guerin, S., Barr, S., Charles, E. and Thompson, N. (2006). 'My Way or the Highway: a More Naturalistic Model of Altruism Tested in an Iterated Prisoners' Dilemma'. Journal of Artificial Societies and Social Simulation 9(2) \<http://jasss.soc.surrey.ac.uk/9/2/4.html\>. Agar, M., Guerin, S., Holmes, R., Kunkle, D., (2004). Epidemiology or Marketing? The Paradigm-Busting Use of Complexity and Ethnography. In: Proceedings of Agent 2004:Challenges in Social Simulation Guerin, S. (2004). Peeking into the black-box: Some art and science to visualizing agent-based models. Proceedings of the 2004 Winter Simulation Conference R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds. Gambhir, M., Guerin, S., Kauffman, S., Kunkle, D. (2004) Steps toward a possible theory of organization. In: Proceedings of International Conference on Complex Systems 2004\. Boston, MA. Guerin, S. and Kunkle, D. (2004) Emergence of constraint in self-organizing systems. Journal of Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 8, No. 2, April, 2004\. Gambhir, M., Guerin, S., Kunkle, D., and Harris, R. (2004) Measures Of Work in Artificial Life. submitted for publication. Boyle, S., Guerin, S., Pratt, J., and Kunkle, D. (2003). Application of agent-based simulation to policy appraisal in the criminal justice system in England and Wales. In: Proceedings of Agent 2003:Challenges in Social Simulation Boyle, S., Guerin, S., and Kunkle, D. (2006) . An Application of Multi-Agent Simulation to Policy Appraisal in the Criminal Justice System. In Chen, S. H., Jain, L., and Tai, C. C. (Eds.), Computational Economics: A Perspective from Computational Intelligence. Hershey, PA : Idea Group \[book chapter\] Dr. George Duncan Journal Articles Exploring the Tension Between Privacy and the Social Benefits of Governmental Databases. Invited paper in Security, Privacy, and Technology edited by Podesta, Shane and Leone. The Century Foundation, 2004 Disclosure Risk vs. Data Utility: The R-U Confidentiality Map as Applied to Topcoding. Invited paper in special issue on Data Confidentiality in Chance (joint authored with S. Lynne Stokes), 2004 "Mediating the Tension Between Information Privacy and Information Access: The Role of Digital Government," George T. Duncan and Stephen Roehrig Public Information Technology: Policy And Management Issues, edited by G. David Garson, Idea Group, Hershey, PA 2003 Policy and practice on release of microdata. Proceedings of the 19th CEIES Seminar, “Innovative Solutions in Providing Access to Microdata. Eurostat. Lisbon, 2002 September 26\. "Confidentiality and Statistical Disclosure Limitation" International Encyclopedia of Social and Behavioral Sciences (2001) "Forecasting analogous time series" (with Wilpen L. Gorr and Janusz Szczypula), Principles of Forecasting: A Handbook for Researchers and Practitioners (J. Scott Armstrong, ed.), Norwell, Ma: Kluwer Publishers, 2001 "Bayesian Insights on Disclosure Limitation: Mask or Impute?" (joint-authored with Sallie Keller-McNulty) Proceedings of the International Society for Bayesian Analysis, Crete (2000) "Optimal disclosure limitation strategy in statistical databases: Deterring tracker attacks through additive noise" (joint authored with Sumitra Mukherjee) Journal of the American Statistical Association (2000) **Appendix** **Sample complex systems model** Consider how a nest of ants learns where food is even though an individual ant has no memory nor learning for this task. Our agent-based model of ant foraging behavior for food is presented below. In the model, blue ants emerge from a yellow nest and drop scent particles (pheromones) which are colored yellow. They are random walkers biased to follow a blue nest pheromone gradient. The particle diffuses and evaporates in an active pheromone field with a gradient (hill) that ends up specifying the location of the nest. Once an ant finds food, it changes its behavior and starts dropping blue food pheromones and biases its walk up the yellow nest pheromone gradient. In this system, the ants themselves are not intelligent. They have no internal learning, nor memory. Note that cognition is a property of the system taken as a whole from agents sensing and acting locally with no centralized storage system nor central agent. We ask that you keep this example in your mind as we outline our strategies for building a data platform for a city. This algorithm along with a few others are the basis of how intelligence and coordination can emerge in a decentralized network. [![][image17]](http://bit.ly/AgentScriptAnts) **Example of a complex adaptive system:** Our agent-based model of the collective intelligence of ants constructing a shortest-time path to food using a decentralized self-organizing algorithm. While the above is a GIF recording, you can see our AgentScript version [running in your browser here](http://bit.ly/AgentScriptAnts) [image1]: <[BASE64-STRIPPED]> [image2]: <[BASE64-STRIPPED]> [image3]: <[BASE64-STRIPPED]> [image4]: <[BASE64-STRIPPED]> [image5]: <[BASE64-STRIPPED]> [image6]: <[BASE64-STRIPPED]> [image7]: <[BASE64-STRIPPED]> [image8]: <[BASE64-STRIPPED]> [image9]: <[BASE64-STRIPPED]> [image10]: <[BASE64-STRIPPED]> [image11]: <[BASE64-STRIPPED]> [image12]: <[BASE64-STRIPPED]> [image13]: <[BASE64-STRIPPED]> [image14]: <[BASE64-STRIPPED]> [image15]: <[BASE64-STRIPPED]> [image16]: <[BASE64-STRIPPED]> [image17]: <[BASE64-STRIPPED]>