IDRC Summit in Istanbul

Notes from the 6th IDRC Summit in Istnabul

The 6th IDRC Summit, a quasi-annual gathering of partners, took place in Istanbul between 04 March and 07 March 2016. Besides being an occasion for discussions on specific projects and plans, the meeting rendered a 'Bayesian' reinforcement of IDRC guiding principals.

As IDRC entered its 7th year since its conception in 2009, specific off-the-shelf products mature, revenue stream grow steadily & surely, and the partners get more entrenched with the clients' engagements as well as with the market in general. Besides the time & effort based core consulting, Murekkep, a Turkish forum was created and handed over, to the CEO & IDRC affiliate Mr Taylan Torin, for business development. It is an innovative bespoke website with superior analytical feature and unique user experience. Check out beta.murekkep.io and relish the experience created by Konstantin Pan and Saurabh Agrawal.

Also a few more ideas, in pipeline for organic adoption & acceptance by the market, were discussed and approved:

Babe Me Up!: An app for women to organise their wardrobe. IDRC is developing this as an affiliate program, for the promoter who is seed funding it.

Tom's Lists (formerly wikilists and more formerly listomania): At the summit, the idea was refined, re-defined, and adopted by Tom Maier. We have a very innovative idea of crowdsourcing all possible lists in the world (will be ever have 'the list of all the lists'?) Up to 4-dimensional lists will be made possible... and held it there, for simplicity sake. Watch out for this one as it emerges organically from the pipeline in the coming days.

Surat Auto: A kind of lean taxi aggregator where IDRC will possibly move with the help of busines leadership and seed funding from an affiliate.

Also at the summit:

Thomas Maier gave a presentation on the institutional investment business, provided an economists perspective on several day-to-day emotions.

Taylan Torin, IDRC affiliate on Murekkep, explained the centrally planned automobile initiatives in Turkey, and also shared his growth strategy for Murekkep.

IDRC had a close interaction with Prof Buket Avci of SMU Singapore, an expert in quantitative operations and process management. Possibilites of her synergistic affiliation with IDRC were discussed.

Further, some corporate level resolutions were made in the long term interest of the company.

History of past IDRC Summits

2011 London: "Diversity & Direction" Thomas Maier, Amit Batra, Alexey Pan, derived the the tenets of IDRC's long-term vision.

2012 Athens: "New Strategies on Classical Foundation" Alexey Pan gave a presentation on the deep relationship of Business and Philosophy.

2013 Frankfurt: "Networked Growth" Thomas Maier elaborated on "Structures for Long-Term incentives".

2014 New York: "Managing the Expexted and the Unexpected" Discussion topics included "Special, client-specific work formats, arrangements, and requests and absorbing them seemlessly.

2015 Bad Homburg: "Taylor-Made Structures" Presentation by an industry expert on "Explainabilty and Consistency as Business Principles".

2016 Istanbul: "The next Level" Amit Batra presented on the Topic "Unlock the 10X".

IDRC Job Opening for Hadoop Data Scientist (Update: Now closed)

Requirement for one Hadoop Data Scientist

Industrial Data Research Corporation, IDRC (www.idrcglobal.com) is a multinational consulting alliance and a subject matter experts in data sciences, analytics, quant modelling, scientific computing, visualizations & infographics, and applied technology.

Our client is a very well established enterprise software & services provider to all the major telecom companies in SE Asia. Our existing team working for this client needs an expansion towards Hadoop related assignments. The work is expected to involve Hadoop cluster set-up & installation, maintenance & upgrade, running ad-hoc queries against the cluster, developing and running full online analytics.

We are looking for a 'tech versatile' data scientist. The person should have 4-7 years of total experience, with at least 2 years of specific experience in Hadoop. The position is full-time and is based out of either Gurgaon or Chandigarh, our two centers.

Candidates meeting the requirements should expect top-decile of industry benchmarked remuneration. To apply, candidates should essentially submit (on talent@idrcglobal.com): (1) a case study showcasing their work and expertise in Hadoop, (2) updated CV, (3) a cover letter that communicates with us to present the case, and establishes suitability, (4) any other information is optionally welcomed.

Candidates are strongly suggested to create a descriptive profiles on LinkedIn and follow IDRC on LinkedIn. This will keep you posted on our activities, including job posts. We regularly visit the list of IDRC followers to match them with our relevant promotions and campaigns.

Note that due to capacity constraints, only short-listed candidates will be contacted back.

Computing for the Balance

Computing for the Balance

The rise of computation economics as seen the development of Computable General Equilibrium (CGE). CGE models are a class of economic models that use actual economic data to estimate how an economy might react to changes in policy, technology or other external factors. CGE models are also referred to as AGE (applied general equilibrium) models. CGE models typically consist of:

(a) equations describing model variables, and,

(b) a database (usually very detailed) consistent with the model equations.

The equations tend to be neo-classical in spirit, often assuming cost-minimizing behaviour by producers, average-cost pricing, and household demands based on optimizing behaviour. However, most CGE models conform only loosely to the theoretical general equilibrium paradigm. For example, they may allow for scenarios such as:

  • non-market clearing, especially for labour (unemployment) or for commodities (inventories),

  • imperfect competition (e.g., monopoly pricing),

  • demands not influenced by price (e.g., government demands),

  • a range of taxes,

  • externalities, such as pollution.

Database design

A CGE model database consists of:

  • tables of transaction values, showing, for example, the value of coal used by the iron industry. Usually the database is presented as an input-output table or as a social accounting matrix. In either case, it covers the whole economy of a country (or even the whole world), and distinguishes a number of sectors, commodities, primary factors and perhaps types of household.

  • elasticities: dimensionless parameters that capture behavioural response. For example, export demand elasticities specify by how much export volumes might fall if export prices went up. Other elasticities may belong to the Constant Elasticity of Substitution class. Amongst these are Armington elasticities, which show whether products of different countries are close substitutes, and elasticities measuring how easily inputs to production may be substituted for one another. Expenditure elasticities show how household demands respond to income changes.

CGE models are descended from the input-output models pioneered by Wassily Leontief, but assign a more important role to prices. Thus, where Leontief assumed that, say, a fixed amount of labour was required to produce a ton of iron, a CGE model would normally allow wage levels to (negatively) affect labour demands.

CGE models derive too from the models for planning the economies of poorer countries constructed (usually by a foreign expert) from 1960 onwards. Compared to the Leontief model, development planning models focused more on constraints or shortages—of skilled labour, capital, or foreign exchange.

CGE modelling of richer economies descends from Leif Johansen's 1960 MSG model of Norway, and the static model developed by the Cambridge Growth Project in the UK. Both models were pragmatic in flavour, and traced variables through time. The Australian MONASH model is a modern representative of this class. Perhaps the first CGE model similar to those of today was that of Taylor and Black formulated in 1974.

CGE models are useful whenever we wish to estimate the effect of changes in one part of the economy upon the rest. For example, a tax on flour might affect bread prices, the CPI, and hence perhaps wages and employment. They have been used widely to analyse trade policy. More recently, CGE has been a popular way to estimate the economic effects of measures to reduce greenhouse gas emissions.

CGE models always contain more variables than equations—so some variables must be set outside the model. These variables are termed exogenous; the remainder, determined by the model, are called endogenous. The choice of which variables are to be exogenous is called the model closure, and may give rise to controversy. For example, some modellers hold employment and the trade balance fixed; others allow these to vary. Variables defining technology, consumer tastes, and government instruments (such as tax rates) are usually exogenous.

Today there are many CGE models of different countries. One of the most well-known global CGE is the GTAP model of world trade.

CGE models are useful to model the economies of countries for which time series data are scarce or not relevant (perhaps because of disturbances such as regime changes). Here, strong, reasonable, assumptions embedded in the model must replace historical evidence. Thus developing economies are often analysed using CGE models, such as those based on the IFPRI template model.

Comparative-static and dynamic CGE models

Many CGE models are comparative-static: they model the reactions of the economy at only one point in time. For policy analysis, results from such a model are often interpreted as showing the reaction of the economy in some future period to one or a few external shocks or policy changes. That is, the results show the difference (usually reported in percent change form) between two alternative future states (with and without the policy shock). The process of adjustment to the new equilibrium is not explicitly represented in such a model, although details of the closure (for example, whether capital stocks are allowed to adjust) lead modellers to distinguish between short-run and long-run equilibria.

By contrast, dynamic CGE models explicitly trace each variable through time—often at annual intervals. These models are more realistic, but more challenging to construct and solve—they require for instance that future changes are predicted for all exogenous variables, not just those affected by a possible policy change. The dynamic elements may arise from partial adjustment processes or from stock/flow accumulation relations: between capital stocks and investment, and between foreign debt and trade deficits. However there is a potential consistency problem because the variables that change from one equilibrium solution to the next are not necessarily consistent with each other during the period of change.

Recursive-dynamic CGE models are those that can be solved sequentially (one period at a time). They assume that behaviour depends only on current and past states of the economy. Alternatively, if agents' expectations depend on the future state of the economy, it becomes necessary to solve for all periods simultaneously, leading to full multi-period dynamic CGE models. Within the latter group dynamic stochastic general equilibrium models explicitly incorporate uncertainty about the future.

Solution Techniques

Early CGE models were often solved by a program custom-written for that particular model. Models were expensive to construct, and sometimes appeared as a 'black box' to outsiders. Today most CGE models are formulated and solved using one of the GAMS or GEMPACK software systems. AMPL, Excel and MATLAB are also used. Use of such systems has lowered the cost of entry to CGE modelling; allowed model simulations to be independently replicated; and increased the transparency of the models.

At IDRC we employ market-leading models, tools, and techniques to achieve tangible returns on analytics. For information, write to us on info@idrcglobal.com