Challenges Encountered and Lessons Learned. Complex and ambitious projects such as Pathways 2050 always face quite a number of challenges. Effective collaboration and dialogue is always time consuming; this was the case for Pathways 2050 also. The identification of time limits to be spent in dialogue is of critical manner. Another challenge is to try to keep the ethos of the project alive, despite changes in personnel. In addition, although tools 57 xxxxx://xxx.xxx.xx/2050-pathways-analysis 58 xxxx://xxx.xxxxxxx.xxx.xx/2050-pathways-public-dialogue/ and technologies may seem easy to use by experts, they might still be difficult for open public; facilitators are always of use. Last but not least, keeping the interest towards the initiative alive for a long time (especially after the initial success) is also a challenging task. Based on the Pathways 2050 experience up to today, there are many lessons that have been learnt and can be offered as recommendations. One of the core lessons learnt was that there is a need to involve stakeholders as early as possible. In addition, being open and transparent is estimated by end users. Collaborative working is also one of the main assets of every large scale project, provided that proper people have been selected for each position. The 2050 Pathways team included members from government, industry, NGOs, academia etc. Moreover, actual innovation can really excite people and make them efficient. In addition, it was concluded that if something is designed in a correct and efficient manner, it could find acceptance to audiences that were not targeted at the beginning. A dynamic, instead of a static approach is also more possible to find acceptance in the end users. Additionally, “be simple” is another lesson learnt; simplifying things helps both stakeholders and end users.
Challenges Encountered and Lessons Learned. The main challenge in the GLEAM case has to do with sustainability. The research effort so far has sustained the tool, but at a certain point policy makers need to provide funding for maintaining such ICT computational tools for actual policy making, as it is happening with other product categories. Only few agencies have small crisis management units that can maintain such tools. Many big agencies do not have a computational or modelling unit and this requires a change of culture from the institution and the agencies. The first lesson learnt is that the use of Web2.0 technologies for the policy-making domain is not an easy task, as policy makers are not used to work with these tools. There is some scepticism or in some other cases too much trust. These computational tools are quantitative and it can be taken for granted that policy making cannot be done solely by looking at the numbers. What needs to happen is to complement the policy making process with this quantitative information but neither to disregard nor underestimate the value of such information. This is due to the fact that behind each model there are assumptions, modelling compromises, incomplete/ missing data, etc. Based on the above, more and more accurate data is always needed. Policy makers are used to work with data not suited for quantitative use. This way the modeller might end up with very rough statistics that are not proper for precise calculations. The same applies for forecasting; the better data you have, the better and more accurate the forecast will be. With very poor data you might get a very disturbed 67 Xxxxxxx Xxxxxxx, M., Xxxxxxx, P., Xxxxxxx, C., Xxxxxxx, J., Xxxxxx, D., Xxxxxxxxx, B., Xxxxx, N., Xxxxxxx, V., Xxxxxxxxxx, A., (2012). Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm, BMC Medicine, 10:165. picture of the future. So one needs to deal with how we can improve and create a culture in the policy making environment for real-time high quality data. It is also surprising that satellites are available for weather, but there is no such map for human mobility (so in the case of GLEAM it had to be created from scratch). The actual technology was there but it was not used to get this kind of data. People in the policy making environment need to understand that those data can provide an edge to the decision making process. The major key success factor of GLEAM has definitely been the fact that the predictions regarding H1N1 pandemic w...
Challenges Encountered and Lessons Learned. Challenges that the Opinion Space teams need to tackle with are of various natures. Firstly, the Opinion Space platform performs a lot of actions so maybe a lighter version should be considered. In terms of policy makers, many concerns on privacy have been raised; different regulations regarding data make things even more complex. In addition, when introducing a new concept/ technology, users might be reluctant in using it. Last but not least, the choice to implement the platform on Flash has led to loss of all Apple-devices users. In terms of risks, two principal risks can be identified:
(i) implementation – the result might not be the desired or requested one,
(ii) not well structured ideas/ questions (e.g. what is the meaning of life) or inability to refer to the proper audience may result to failure in participation. Regarding lessons learnt, above all is that the slightest effort needed by the user really affects participation; everything needs to be easy and user friendly. For example, by increasing the start- questions from 5 to 8, participation decreased almost 50%. In addition, the Opinion Space team learnt more about machine learning techniques and algorithms and their capabilities and sensitivities. From the policy makers point of view (State Department to be more specific), they learnt that ideas can be very diverse and scattered; and many times this is neglected by media and press. In addition, when someone applies social media systems in policy making procedures he always seeks for the lightest application possible, which works across all platforms (operating systems, mobile devices) and is easily set and operated. Regarding the overall Opinion Space concept, one can say that the platform works particularly well when you apply it to a specific use case or/ and a well-formulated idea.
Challenges Encountered and Lessons Learned. One of the key lessons learnt from the UrbanSim case has been the fact that having a balance between academic research (mostly funded by NSF but also others) and real-world applications towards producing real working systems, is a really effective approach. Moreover, when dealing with such applications, it is very important to engage people from the early steps of the project, in order to get them familiar with the tool and acquire as much feedback data as possible. Another recommendation deals with early engagement; policy makers and end users (e.g. expert consultants) in general need to be engaged in the project as soon as possible. Adequate time for the development of the models and testing of the models is also necessary. Additionally, finding out a new public engagement strategy, more capable on avoiding public disruption movements is also advisable (probably through smaller meeting and interaction through the web), as public disruption was eventually an unexpected challenge. Some of the main challenges faced were the very short time frames of the project (regarding its implementation) and having rather poor data available from agencies to begin the project. Having too much software development at the same time was another (probably inevitable in such projects) risk.
Challenges Encountered and Lessons Learned. Question 4.1 - What are the key lessons learned? Which were the key success factors and drivers that enabled positive developments?
Question 4.2 - What are the main drawbacks of your case and the barriers you faced during implementation?
Question 4.3 - Which recommendations would you provide based on the experience gained in your case? Which risks have you identified and should be taken under consideration? How to overcome the barriers faced?
