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There is a recognition that trade costs along domestic and international supply chains can be significantly reduced by improving the logistics performance in each mode of transport involved in various chain transactions. These improvements may be conveniently facilitated by the optimisation of the transitioning strategies from unimodal to multimodal (or combined) transport services. In this context, we examine here the status of current multimodal connectivity in freight transport practiced in the ASEAN region. We have focussed on the potential economic impacts of enhancing the levels of supply chain multimodal connectivity on the emerging economies of this region.

The Australian National Outlook is a new initiative by CSIRO, which is intended to contribute to the evidence base and understanding required for Australia to navigate the complex and often intertwined challenges involved in achieving sustainable prosperity. This first National Outlook seeks to provide a better understanding of Australia’s physical economy. It has a particular focus on understanding two aspects: The ‘energy-water-food nexus’ and the prospects for Australia’s materials and energy-intensive industries, which account for one quarter of economic value and employment, but around three quarters of our use of energy, water and materials. The analysis explores over 20 possible futures for Australia out to 2050 against the backdrop of the past 40 years to identify key future global drivers and assesses how these may impact our country. It then integrates these global perspectives into a uniquely Australian context in relation to plausible technological and policy settings we must consider as a nation to secure our future prosperity. The National Outlook and science in general can contribute evidence and analysis to inform the national conversations. However, it cannot determine the choices we have to make as a community. They will – and should – be shaped by our values and collective imagination. While this outlook identifies national opportunities, achieving these benefits will require considerable further deliberation and action. The investments and other changes required will not happen overnight. There is no overstating the challenges that exist for policymakers, industries and communities in navigating the transitions needed to secure our future prosperity.

We introduce a modification to the two-timescale games studied in the evolution of preferences (EOP) literature. In this modification, the strategic process occurring on the long timescale is learning by an individual across his or her lifetime, not natural selection operating on genomes over multiple generations. This change to the longer timescale removes many of the formal difficulties of EOP models and allows us to show how two-timescale games can provide endogenous explanations for why humans sometimes adopt interdependent preferences and sometimes exhibit logit quantal response functions. In particular, we show that our modification to EOP explains experimental data in the Traveler´s Dilemma. We also use our modification to show how cooperation can arise in nonrepeated versions of the Prisoner´s Dilemma (PD). We then show that our modification to EOP predicts a crowding out phenomenon in the PD, in which introducing incentives to cooperate causes players to stop cooperating instead. We also use our modification to predict a tradeoff between the robustness and the benefit of cooperation in the PD.

In this paper, we reanalyze Medical Innovation, the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors¿ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis.