THE SECOND dimension is related to the ultimate goal of democratic public policy itself that is Human Dignity, as Laswell declared that the policy scientist of democracy just “decided” on democracy and its “ultimate goal” of human dignity (Laswell, 1951). Again, post positivists point positivism is the paradigm that ignoring human dignity I all its process of thinking. Think mathematically in policy process violate or harm democracy ethic, because it simplify individual existence of human being just to numbers and formula. Individual and cultural expressions, interests, unique, and dignity are ignored in mathematical and numerical base policy making. In this part, public policy loose its humanity sense because replace dynamic social life into mathematical pattern.
However, modern positivists belief that without standardized reason in the policy making process the product of policy can easily manipulated by elite who have strong access to policy process. It is harm human and people dignity. Math can provide a good standardized reason for policy making. For example, in allocating our budget (APBN/D) we mostly only use two methods, one is just follow the proportion of previous budget and the second is bureaucratic polity, that is depends on how ‘creative’ the leader of the department lobby the politicians so their department get more money than another. Both are horrible. By using whether calculus or linear programming, for instance, in terms of making our national or local budget, we will know precisely which department should get more in this year and which one is not depends on what sort of constraints we have. In this context, math provides objectivity, no matter how strong the leader of certain department in their bureaucratic polity world, if math shows that this year his/her department budget needs to be reduced, so it will be reduced.
The last dimension is whether math tends give the truth or lie. Darrel Huff, more than ten years ago had ‘reveal’ one the biggest lie in the world that is statistics (Huff, 1993). Since making the assumption, sampling and until define parameters we can easily manipulate the result of statistical numbers into our interest. Those are the proves of how positivism is anti-democracy the statistical data that they belief as the ‘facts’ are manipulated, not to mention how big the bias of data interpretation could be appears. We have a very good example of this. Last year president SBY announced that the number of poverty in Indonesia is 15.97%. Statistically that is true, but when we try to lift up the line from $1 per day to $2 per day, we will see the extreme different percentage that is 26.15%, that is what the World Bank called as potentially poor, even though the difference of quality of life between $1/day person with $2/day person is just the same.
Maybe it is true that statistic can lie but it is not the only one, politician and media can make lies more than statistic can do. Therefore, if post-positivists pay more attention on public opinion, public interest or voice of the people, the question is where can we get that? It could be from media but do we believe on the neutrality of media? From some people who do act in the street? Are those people represent the majority of 250 million people of Indonesia? or from politician and political party? Lie with statistic is not a good thing, but still its better than lie by using hegemonic power of media, money or politic.
Math in Democratic Public Policy Process
The word ‘math’ has very strong impression for everybody whom hears it. This word always reminds people to numbers, formula, fancy graphs and complicated equations. Actually, those things are only the little part of math; math is more about language and logic. If you face problem in your daily life, and this problem are not simple. But you need to explain this problem to other people and, therefore, you also want structuring the problem, pick some important facts and make the connections between those facts; at that time you already do math.
And then if you want to solve that problem, you start to make the assumption, using some way of thinking and put your facts into that way of thinking to find the solution, you already do math. Therefore, applying quantitative analysis means embracing mathematical logics and techniques for their capacity for structuring information and communicating insights, while putting less emphasize on literal precision on numerical answer (Wong, 2007).
Moreover, math is the best way to achieve objectivity in this civilization. Post-positivists claim that there is no objective truth in this world, but there are inter-subjectivity. That is true, however, we are not talking about objectivism as a truth but objectivism as impersonality. This nature of math be able to detach ourselves by both ignoring personal relevance and focusing on the substance of the issues rather than the personalities involved. A society that values openness and democratic ideals requires that information be conveyed in the most objective and most quantifiable possible form for the sake of public accountability (Porter, …?).
So, if post-positivists worry about how difficult mathematical logic to understood by people is, why we don’t educate people so they be able to follow that logic? If they are worry about how complicated the mathematical language is? Why we don’t encourage the policy analysts to simplify their language so can be easily understood by people? Eventually, we will achieve what the Jurgen Habermas’ ideal democracy that is quantitatively participative and qualitatively discursive. Pushing up public participation without educate the people is nothing but increasing anarchy, because this effort forgot to build the qualitative discursive part.
Lasswell, Harold D. 1951. “The Policy Orientation.” In The Policy Sciences: Recent Developments in Scope and Method, eds. Daniel Lerner and Harold D. Lasswell. Stanford: Stanford University Press.
Huff, Darrell, 1993, How to Lie With Statistics, W. W. Norton & Company
Shullock, N, 1999, the paradox of policy analysis, Journal of Policy Analysis and Management, Vol. 18, No. 2, 226–244 Published by John Wiley & Sons, Inc.
Farr, J., Hacker J. S., Kazee N., The Policy Scientist of Democracy: The Discipline
of Harold D. Lasswell American Political Science Review Vol. 100, No. 4 November 2006
Wong, P., 2007, Lecture Note of Introduction to Quantitative Analysis, LBJ School of public Affairs
by Fadillah Putra
1st year LBJ School of Public Affairs Student University of Texas at Austin