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//"While any new technical device or medical drug has extensive testing for efficiency, reliability and safety before it ever hits the market, we still implement new economic measures without any prior testing"// Dirk Helbing 2009

//"Creating a carefully crafted agent-based model of the whole economy is, like climate modelling, a huge undertaking."// Farmer & Foley 2009



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====Simulation of Order Fulfillment in Divergent Assembly Supply Chains====
|Troy J. Strader, Fu-Ren Lin and Michael J. Shaw (1998)| JASSS vol. 1, no. 2| [[http://www.soc.surrey.ac.uk/JASSS/1/2/5.html|html]]|

** Abstract**: Management of supply chains is a difficult task involving coordination and decision-making across organizational boundaries. Computational modeling using multi-agent simulation is a tool that can provide decision support for supply chain managers. We identify the components of a supply chain model and implement it in the Swarm multi-agent simulation platform. The model is used to study the impact of information sharing on order fulfillment in divergent assembly supply chains (commonly associated with the computer and electronics industries). We find that **efficient information sharing enables inventory costs to be reduced** while maintaining acceptable order fulfillment cycle times. This is true because information, which provides the basis for enhanced coordination and reduced uncertainty, can substitute for inventory. 

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====Tutorial on Agent-Based Modelling and Simulation Part 2: How to model with agents====
|C. M. Macal, M. J. North, 2006| Proceedings of the 2006 Winter Simulation Conference| 

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====An agent based approach to analysis of Capital structure and industry dynamics: The role of policy====
Roberto GABRIELE 
Enrico ZANINOTTO

**Abstract:** The paper analyses the role of capital structure of firm on industry dynamics. Empirical evidence shows that policy interventions can alter for some the frims??cost of the capital. As a result firms on average are overcapitalized with a low capital productivity. Moreover, the market in which capital is cheap seems to be able to reduce consistently the entry and the exit of firms: the competitive process is obscured and selection is sterilized. We argue that the distortions induced by an anomalous endowment of capital input can have two effects in the market: (a) A raise in firmsï¿½ heterogeneity; (b) a distortion of the process of acquisition of technical progress. We used an agent based model to explore the issue. Firms in the model are heterogeneous bounded rational and endowed with a two inputs fixed coefficients technology. Technical progress is embodied in the new capital stock and modifies the combination of the two inputs. The cost of the capital input is one of the relevant parameter of the model. Each capital stock can have short (SL) or long life (LL). The policy interventions finance the LL investments so that its price will be on average lower than for SL. Firms have to choose in each period between two strategies: A cost reducing one??in the present choosing to invest in LL-, or a productivity augmenting oneï¿½as a stochastic outcome of the investment in SL. The model is programmed using SWARM platform and it is explored through Montecarlo simulations.

====Others====

[[http://www.nature.com/nature/journal/v460/n7256/index.html|Nature Volume 460 Number 7256 pp667-772]]

AKKERMANS, H (2001), Emergent Supply Networks: Simulation of Adaptive Supply Agents. Proceedings of the 34th Hawaii International Conference on System Sciences, January 3-6, Maui, HI, 11 pp.

DWYER, R, Schurr, P and Oh, S (1987), Developing buyer-seller relationships. Journal of Marketing, 51, April 1987, pp. 11-27. 

KIMBROUGH, S O, Wu, D J and Fang, Z (2002), Computers play the beer game: can artificial agents manage supply chains?. Decision Support Systems, 33(3), pp.323-333.

LIN, F, Sung, Y and Lo, Y (2005), Effects of trust mechanisms on supply-chain performance, International Journal of Electronic Commerce, 9(4), pp. 91-112.

MACAL, C M (2004), Emergent structures from trust relationships in supply chains, Proceedings of Agent 2004 Conference, October 7-9, Chicago, IL, pp. 743-760.

PATHAK, S, Day, J, Nair, A, Sawaya, W and Kristal, M (2007), Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective. Decision Science, 38(4), pp. 547-580.

SCHIERITZ, N and Größler, A (2003), Emergent Structures in Supply Chains: A Study Integrating Agent-Based and System Dynamics Modeling. Proceedings of the 36th Hawaii International Conference on System Sciences, January 6-9, Big Island, HI, 9 pp.

CHOI, T, Dooley, K and Rungtusanatham, M (2001). Supply Networks and Complex Adaptive Systems: Control versus Emergence. Journal of Operation Management 19, pp. 351-366.

STRADER, T J, Lin, F and Shaw, M J (1998), Simulation of Order Fulfillment in Divergent Assembly Supply Chains. Journal of Artificial Societies and Social Simulation 1(2) 5 http://jasss.soc.surrey.ac.uk/1/2/5.html.

TYKHONOV, D, Jonker, C, Meijer, S and Verwaart, T (2008), Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks. Journal of Artificial Societies and Social Simulation 11(3) 1 http://jasss.soc.surrey.ac.uk/11/3/1.html.

VLACHOS, P I and Bourlakis, M (2006), Supply chain collaboration between retailers and manufacturers: Do they trust each other?. Supply Chain Forum: An International Journal, 7(1), pp. 70-80. 

Whan-Seon Kim (2009)
Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study
Journal of Artificial Societies and Social Simulation 12 (3) 4
<http://jasss.soc.surrey.ac.uk/12/3/4.html>