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:
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? One of the core lessons learned was that there is a need to involve people as early as possible; the early involvement of all kind of stakeholders was really helpful in the case. In addition, it helps to be open; people appreciate openness and transparency. Collaborative working was also fruitful and constituted a real benefit. Moreover, actual innovation can really excite people and make them efficient. In addition, we learned that if you design something really well, it could find acceptance to audiences you did not have in mind 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. Of course, recruiting the right people for the right position is critical for the success of each project. The team included members from government, industry, NGOs, academia etc. As a high level conclusion also, without the internet, the project wouldn’t have meaning, wouldn’t have been implemented at all. Last but not least, you have to keep reminding people why you are doing what you are doing.
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? The first lesson learned 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 but 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 not to disregard neither to underestimate the value of such information, because behind each model there are assumptions, modelling compromises, missing data, etc. Also more and accurate more data is needed. Policy making used to work with data not suited for quantitative use. This way you 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, while with very poor data you might get a very disturbed picture of the future. So one needs to deal with how we can improve and create a culture in the policymaking environment for real-time high quality data. It is also surprising that we have satellites for weather, but there is no map for human mobility (so in the case of GLEAM we had to create it from scratch). The technology was there but not used to get his data. People in the policy making environment need to learn that those data can provide an edge to the decision making process. The major key success factor has been the fact that we were good enough with the H1N1 pandemic predictions, while working in real time, which helped to build a dialogue with the policy makers, starting a trust relationship. Of course success means providing something that no one else can provide. There are many groups working in different levels (local/regional/country) and we went into a global level, not reinventing the wheel but specialising and providing different things.
