In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The 2008 crash has left all the established economic doctrines - equilibrium models, real business cycles, disequilibria models - in disarray. Part of the problem is due to Smith’s "veil of ignorance": individuals unknowingly pursue society’s interest and, as a result, have no clue as to the macroeconomic effects of their actions: witness the Keynes and Leontief multipliers, the concept of value added, fiat money, Engel’s law and technical progress, to name but a few of the macrofoundations of microeconomics. A good viewpoint to take bearings anew lies in comparing the post-Great Depression institutions with those emerging from Thatcher and Reagan’s economic policies: deregulation, exogenous vs. endoge- nous money, shadow banking vs. Volcker’s Rule. Very simply, the banks, whose lending determined deposits after Roosevelt, and were a public service became private enterprises whose deposits determine lending. These underlay the great moderation preceding 2006, and the subsequent crash.

The Economic Institutions of Capitalism

The four Visegrad states — Poland, the Czech Republic, Slovakia (until 1993 Czechoslovakia) and Hungary — form a compact area between Germany and Austria in the west and the states of the former USSR in the east. They are bounded by the Baltic in the north and the Danube river in the south. They are cut by the Sudeten and Carpathian mountain ranges, which divide Poland off from the other states. Poland is an extension of the North European plain and like the latter is drained by rivers that flow from south to north west — the Oder, the Vlatava and the Elbe, the Vistula and the Bug. The Danube is the great exception, flowing from its source eastward, turning through two 90-degree turns to end up in the Black Sea, forming the barrier and often the political frontier between central Europe and the Balkans. Hungary to the east of the Danube is also an open plain. The region is historically and culturally part of western Europe, but its eastern Marches now represents a vital strategic zone between Germany and the core of the European Union to the west and the Russian zone to the east.

Economy and Society

On blockchain and its integration with IoT. Challenges and opportunities

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading models of man as one of the reading material to finish quickly.

Models of man

Blockchain technology has emerged as a solution to consistency problems in peer to peer networks. By now, it has matured as a solution to a range of use cases in which it can effectively provide the notion of third party trust without the need for a trusted (physical) third party, which makes it an attractive coordination mechanism for distributed systems. To promote the wide adoption of this technology, we yet lack mechanisms that make the specification and interpretation of smart contracts accessible to a broader audience. In this work, we propose a modeling approach that supports the semi-automated translation of human-readable contract representations into computational equivalents in order to enable the codification of laws into verifiable and enforceable computational structures that reside within a public blockchain. We identify smart contract components that correspond to real world institutions, and propose a mapping that we operationalize using a domain-specific language in order to support the contract modeling process. We explore this capability based on selected examples and plot out directions for future research on smart contracts.

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From Institutions to Code: Towards Automated Generation of Smart Contracts

Institutions, as manifestations of social behaviour, are crucial elements of any society’s structure and are widely studied in the area of New Institutional Economics as a determinant of a society’s development and prosperity. Although taking promising steps by considering a wider array of behavioural influence mechanisms, to date analytical approaches primarily examine institutions from the perspective of stable equilibria, which are assumed as indicative for the existence of institutions. In this work we shift the focus from equilibria to an interactionist perspective, and apply Agent-Based Modelling (ABM) for the purpose of institutional analysis. ABM offers the ability to represent institutions in great detail by using modelling metaphors that are in close alignment with real observable social structures and interaction patterns, as opposed to concentrating on rational strategy choices of structurally uniform individuals. With ABM we can leverage a sociological perspective and “grow” institutional structures from the bottom up, instead of just interpreting those from a bird’s eye perspective. This work not only applies ABM to institutional analysis, but also develops it further as a tool for the representation and systematic analysis of institutions. Essential contributions pointing in this direction include a refined generalised institution representation (nADICO) that captures the complexity of institutions. We further propose a continuous norm concept (Dynamic Deontics) that models the formation and change of normative understanding based on environmental stimuli. A final extension is the analysis of normative understanding on different sociological levels using Interval Type-2 Fuzzy Sets. These contributions enrich the computational social scientists’ toolbox with modelling and analysis mechanisms that are a) sociologically grounded, b) facilitate a fine-grained

Agent-Based Institutional Modelling: Novel Techniques for Deriving Structure from Behaviour

We propose a refined institutional scheme derived from Crawford and Ostrom’s Grammar of Institutions (GoI) that has been refined to provide a more comprehensive representation of conventions, norms, and rules, which extends to describing institutions in more detail but also allowing the expression of fuzzy aspects (e.g. the uncertainty about a sanction’s occurrence). After initially reviewing the GoI grammar structure (also referred to as ADICO), we discuss its adoption as well as limitations. We introduce selected extensions and refinements that enable the grammar’s ability to describe institutions in more detail, but also to capture more complex institutions as well as characteristics of institutions themselves, such as institutional regress.

nADICO: A Nested Grammar of Institutions

Agent-based Modelling appears as a promising analytical tool when it comes to a lasting question: in how far did different institutions affect the social and economic outcomes of societies? Taking an incremental step to address this question, we present a refined approach that combines existing institution representations (the structure) with a norm identification process to systematically grow normative understanding from the bottom up without relying on any prior knowledge. The proposed mechanism provides agents with the ability a) to detect complex normative behaviour by developing and differentiating stereotypes of social actors, and b) to generalise behaviour beyond observed social entities, giving agents the ability to develop normative understanding as a potential precursor for predicting newcomers behaviours. We exemplify this approach using a simulated prototypical trader scenario that is evaluated with respect to behavioural diversity (different compositions of non-/cooperative agents) as well as structural diversity (different types of agents). Using the simulation results, we showcase the explanatory power of the derived normative understanding beyond the interpretation of quantitative results, and finally discuss the generalisability of the proposed approach.

/pdf/modelling-dynamic-normative-understanding-in-agent-societies-2upqd75ary.pdf

Christopher Frantz

Papers

From Institutions to Code: Towards Automated Generation of Smart Contracts

Agent-Based Institutional Modelling: Novel Techniques for Deriving Structure from Behaviour

nADICO: A Nested Grammar of Institutions

Modelling dynamic normative understanding in agent societies

Institutional Grammar 2.0: A Specification for Encoding and Analyzing Institutional Design