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Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This study first replicates, then perturbs, the centipede game as implemented by McKelvey and Palfrey (1992). It is thus both a replication study and an original research study. We use controlled laboratory experiments, with computer interfaces for each treatment, anonymous round-robin matching among the subjects across rounds, multiple (10) rounds within each treatment, and incremental changes between adjacent treatments allowing for an assessment of effects at the margin of different game configurations. We find unraveling to the subgame perfect equilibrium somewhat faster than did McKelvey and Palfrey (1992), when using their exact design. Perturbations to that design show that setting non-taker payoffs to zero induces earlier unraveling, as does the use of higher stakes (as in Murphy, Rapoport, and Parco (2006), and Rapoport, Stein, Parco, and Nicholas (2003), respectively). Other, subsequent perturbations show: that there is at most a subtle effect associated with using a 10-second timer with a default move, relative to untimed active moves; and that clock format versus tree format has a minimal effect in common information, unchanging payoff-parameterization environments. We verify the robustness of some key past findings in real-time games. We also explore in a common information environment, the effect of design features previously used in independent private values settings; here we find new evidence that features which might modulate information acquisition and/or processing in an independent private values setting may not restrict behavior in a common information setting.

Shalev's third suggested path for progress consists of using tables, graphs, and tree diagrams to examine causal hierarchy and complexity and to identify cases meriting more in-depth scrutiny. This should be viewed not as (or at least not solely as) a substitute for regression but rather as a critical component of regression analysis. All of us were (I hope) taught in our first regression course that it is not enough to simply get the data, estimate some regression equations, and then draw conclusions. It also is necessary to get a feel for the data, in large part by examining descriptive statistics and looking at bivariate and/or multivariate patterns. Too many macro-comparativists, I suspect, either do not do this at all or do not do it sufficiently carefully.