OPTIMAL LAND CONVERSION AND GROWTH WITH UNCERTAIN BIODIVERSITY COSTS

An important characteristic defining the threat of environmental crises is the uncertainty about their consequences for future welfare. Random processes governing ecosystem dynamics and adaptation to anthropogenic change are important sources of prevailing ecological uncertainty and contribute to the problem of how to balance economic development against natural resource conservation. The aim of this study is to examine optimal growth subject to non-linear dynamic environmental constraints. In a two-sector exogenous growth framework we model a stochastic environmental good, exhibiting uncertain ecological responses to environmental change, and describe the economic and environmental trade-offs that ensue for a risk-averse social planner. Allowing for ecological risk tends to slow economic growth if environmental impacts are assumed to increase exponentially as the rate of disturbance increases. Taken in isolation the effects of ecosystem resilience and ecological uncertainty on the rate of natural resource development are ambiguous and depend on normative parameters such as the social planner’s attitude to risk and rate of time preference.

Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems

Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling

Manufacturing Agrarian Change - Agricultural production, inter-sectoral learning and technological capabilities

The aim of this paper is to investigate how industrial development, manufacturing in particular, has been contributing to agrarian change. In order to address this issue, it analyzes the technical bases and structural specificities – i.e. time and scale constraints – of agricultural production. Technical change in agriculture involves both improvements in organic transformation processes – i.e. biological production – and in the mechanical functions that have to be performed for obtaining a certain output – i.e. agricultural work. The paper shows how in-farm technological capabilities building as well as inter-sectoral learning are necessary in order to acquire and adapt biological-chemical innovations and mechanical technologies. The analysis of agrarian technical change – both in-farm learning and inter-sectoral learning – is developed by integrating peasant studies with evolutionary approaches to economic development. The relationship between agrarian change and manufacturing development is highly context specific, thus comparative historical analysis is adopted in order to shed light on the abovementioned processes of learning. Building on the analysis of technological change in agriculture, the last part of the paper will focus on those transformative policies such as innovative ‘extension services’ which facilitate inter-sectoral learning and, in turn, allow the emergence of inter-sectoral commons. This concept identifies that specific bundle of technological capabilities which concentrate in certain areas of strong inter-sectoral interdependence as a result of inter-sectoral learning.

Evolving Strategy: Risk Management and the Shaping of Large Engineering Projects

Large engineering projects (LEPs) are high-stakes games characterized by substantial irreversible commitments, skewed reward structures when they are successful, and high probabilities of failure. Their dynamics also change over time. The journey from initial conception to ramp-up and revenue generation takes 10 years on average. While the “front end” of a project – project definition, concept selection, and planning – typically involves less than one third of the total elapsed time and expense, it has a disproportionate impact on outcomes, as most shaping actions occur during this phase. During the rampup period, the reality of market estimates and the true worth of the project are revealed. Sponsors may find that actual conditions are very different from expectations, but only a few adaptations are possible. Once built, most projects have little flexibility in use beyond the original intended purpose. Managing risks is thus a real issue. The purpose of this chapter is to sketch out the various components of risk and outline ranges of strategies for coping with risks and turbulence based on an assessment of 60 projects as part of the IMEC study. Further more, we propose the elements of a governance system to master their evolutionary dynamics. The main finding is that successful projects are not selected but shaped. Rather than choosing a specific project concept from a number of alternatives at the outset based on projections of the full sets of benefits, costs and risks over the project’s lifetime, successful sponsors start with project ideas that have the potential to become viable. These sponsors then embark on shaping efforts to influence risk drivers ranging from project-related issues to broader governance. The seeds of success or failure of individual projects are thus planted early and nurtured over the course of the shaping period as choices are made. Successful sponsors, however, do not escalate commitments, and they abandon quickly when they recognize that projects have little possibility of becoming viable

The dynamics of games of innovation

Many executives see innovation as an unmanageable process, riddled with risks. The\ud research we conducted with the Industrial Research Institute, interviewing over 200 vicepresidents\ud of research and development and chief technical officers in many sectors around\ud the world, yields a more nuanced view. Innovation becomes manageable once managers\ud move away from normative prescriptions that view the process as uniform and recognise\ud that different rules and practices apply to different circumstances.\ud Our argument is that clusters of interdependent firms contributing to the building of a set of\ud interacting products and services tend to self-organise themselves into distinct and relatively\ud persistent “games of innovatio

A behavioral macroeconomic model with endogenous boom-bust cycles and leverage dynamcis

We merge a financial market model with leverage-constrained, heterogeneous agents with a reduced-form version of the New-Keynesian standard model. Agents in both submodels are assumed to be boundedly rational. The fi nancial market model produces endogenously arising boom-bust cycles. It is also capable to generate highly non-linear deleveraging processes, fi re sales and ultimately a default scenario. Asset price booms are triggered via self-fulfilling prophecies. Asset price busts are induced by agents' choice of an increasingly fragile balance sheet structure during good times. Their vulnerability is inevitably revealed by small, randomly occurring shocks. Our transmission channel of financial market activity to the real sector embraces a recent strand of literature shedding light on the link between the active balance sheet management of financial market participants, the induced procyclical fluctuations of desired risk compensations and their final impact on the real economy. We show that a systematic central bank reaction on financial market developments dampens macroeconomic volatility considerably. Furthermore, restricting leverage in a countercyclical fashion limits the magnitude of financial cycles and hence their impact on the real economy. --behavioral economics,New-Keynesian macroeconomics,monetary policy,agent-based financial market model,leverage,macroprudential regulation,financial stability,asset price bubbles,systemic risk

Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes