Editing Development communication (section)

===== Simulations and modelling =====
Simulations and modelling recently become a useful tool policy analysis involving computers and software in creating a virtual representation of the scenario. Because it offers a systems view of the situation, the analyst or researcher can monitor how the players or variables interacts in the simulated environment. The purposes of simulations may vary to include education, research, design improvement and/or the exploration of the probable effect of different policy decisions.

Guyonne Kalbe(2004) identifies and distinguishes two types of simulation models: macro and micro levels. According to Kalbe, the macro-level is applied mostly for huge sectors of the industries. This macro simulation is usually applied by developed countries in order to assess and understand policy changes. On the other hand, the micro-level is used for a specific company using a sample of population when a need for more precised and focused information is its goal. In contrast to large-scale industries that use the macro-level approach, the micro-level is individualized.<ref>{{Cite journal|last=Kalbe|first=Guyonne|title=Introduction: The Use of Simulation Models in Policy Analysis|url=http://businesslaw.curtin.edu.au/wp-content/uploads/sites/5/2016/05/AJLE-v7n1-kalb-intro.pdf|journal=Australian Journal of Labour and Economics|date=30 January 2020 |volume=7|pages=1–12}}</ref>

Since problems in policy decisions are not linear by nature, computer simulations provides a concrete view of the situation and how the variables changes pace. These changes in behaviors are integral in developing policies. Steven Bankes(1992) explicates the use of computer simulation in policy decisions wherein models used in policy analysis provide arguments to illuminate options for policy decisions based on the result of computer simulated analysis.<ref>Bankes, S. (1992). https://www.rand.org/content/dam/rand/pubs/notes/2009/N3093.pdf</ref>

The methodology has been successfully used in development projects. Thorngate & Tavakoli(2009) mention fields where computer simulations has aided decision makers in assessing the context and solutions to specific problems. Among these include: the climate changes, effects of fiscal changes in economic policies, traffic regulations, health allocation resources, air regulations and crisis management to name a few.<ref>Thorngate, W., and Tavakoli, M. (2009). Simulation, Rhetoric, and Policy Making. Simulation & Gaming, Sage Publications. Volume 40 number 4. https://doi.org/10.1177/1046878108330539</ref>

It is noted that simulations and modelling could be based on artificial data generating process (DGP) or real live data from the environment for analysis. The real data derived from the environment is often called "big data" due to the significantly larger size. This is especially critical in the development communication discussion when there is prevalent use of digital communication technology in low and middle income countries (Taylor & Schroeder, 2015). The technologies in these countries include mobile phones and notebooks. These technologies emit data as a byproduct and have great potential to fill some of the problematic gaps encountered by country policy makers and international development organizations. There is research indicating that the use of big data represents an important complement to country level statistics (Taylor & Schroeder, 2015).,<ref>{{cite journal |last1=Taylor, L & Schroeder R |first1=L&R |title=Is bigger better? the emergence of big data as a tool for international development policy |journal=GeoJournal |date=2015 |volume=80 |issue=4 |pages=503–518 |doi=10.1007/s10708-014-9603-5|bibcode=2015GeoJo..80..503T |s2cid=154360975 }}</ref> better water quality modelling (Korfmacher, 1998)<ref>{{cite journal |last1=Korfmacher |first1=K. S |title=Water quality modeling for environmental management: Lessons from the policy sciences |journal=Policy Sciences |date=1998 |volume=31 |issue=1 |pages=35–54 |doi=10.1023/A:1004334600179|s2cid=189823529 }}</ref> and improved agricultural development (WESTERVELT, 2001).<ref>{{cite journal |last1=WESTERVELT |first1=J.D |title=Empowering stakeholders and policy makers with science-based simulation modeling tools |journal=The American Behavioral Scientist |date=2001 |volume=44 |issue=8 |pages=1418–1437|doi=10.1177/00027640121956764 |s2cid=145768863 }}</ref>

The use of big data can ensure a more accurate measurement of macro-economic data such as price track. The Billion Prices Project (BPP) initiated by MIT's Sloan School of Management challenges the Argentina government on the misleading inflation index report. It reported by very high inflation rate by the government's statistical institute which led to the fire of all government officials in the department a few years later. The actual inflation rate after the lay-off eventually stabilizes. The group in MIT decided to investigate what is going on by programming a web scraper to find prices for everyday goods posted on the web by the country's supermarkets. It scrapes many data on the web and is a financially affordable experiment. The outcome of the result led to an increased suspicion that Argentina's statistical agency was under pressure to level off inflation rate by higher order authority. The BPP proves to be influential because it produced an inflation index that was more intuitively reflective of perceptions and in real society than the government. It also provides an alternative set of perspective on economic trends which policymakers can use to make prudent finance policy decisions. There is an increasing need for major governments in the world to rethink how development statistics should be collated in order to craft better and finer public policy.

The simulation approach in policy science is beneficial to policy coherence on the sustainable development goals commonly called SDGs. The SGDs developed by United Nations has integrative nature which is suitable for integrative modelling techniques (Collste et al., 2017).<ref>{{cite journal |author1=Collste, D. |author2=Pedercini, M. |author3=Cornell, S. E. |title=Policy coherence to achieve the SDGs: Using integrated simulation models to assess effective policies |journal=Sustainability Science |date=2017 |volume=12 |issue=6 |pages=921–931 |doi=10.1007/s11625-017-0457-x|pmid=30147764 |pmc=6086251 |bibcode=2017SuSc...12..921C }}</ref> Collste and the researchers have shown in a Tanzania experiment that modelling approach towards SGDs can bring interlinks to the forefront and facilitate a shift to a discussion on development grounded in systems thinking. It brings the multitudes of possible feedback loops that shape a country's development especially those in developing country. The modelling approach in SDGs maps interlinkages and provide analysis about the resulting behaviour of different policy decisions. It also provide new casual pathways on investments in public projects.