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Lockyear, Kris (1991) 'Simulating coin hoard formation', in Kris Lockyear and Sebastian Rahtz, Computer Applications and Quantitative Methods in Archaeology 1990, pp. 195--206. Oxford: British Archaeological Reports International Series 565.

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This paper examins the factors which impact the contents of coin hoards including the decay rate, the delay in the introduction of coinage to an area, and the manner of hoard collection. Although later work refined and developed this area of
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  Computer Applications and Quantitative Methods in Archaeology 1990 Edited by Kris Lockyear & Sebastian Rahtz with Clive Orton 1 9 a a i ?   Re c T1 1 Julian Richards Nick Ryan BAR International Series 565 1991 Reprinted from  28 Simulating coin hoard formation Kris Lockyear (lnstitute of Archaeology, 31-34 Gordon Square, LondonWC1H OPY) 28.1 Introduction The physical manifestations of we,alth are highly varied, and the ways in which this wealth is obtained, stored, dis- played, used and disposed of is a major area of research for archaeologists, anthropologists and sociologists alike (e.g. Rcnfrew & Shennan 1982). For example, the rOle of amber in the British Early Bronze Age (Beck & Shennan 1991, chapter 6) or copper beads in the Swiss Neolithic (Ottaway & Strahm 1975) has received detailed attention and analysis. Recent developments in theoretical archae- ology have tcnded away from the 'trade and exchange' aspects of certain artefacts and more towards their `sym-bolic meanings.' When, however, coinage appears in the archaeological record an ethno-centric attitude to the data becomes prevalent. Whereas coinage is in fact just another physical manifestation of wealth, be it a highly specific form, phrases such as the `cash' or `monetary economy' enter the literature without qual i fication. lt has been shown that coinage does not perform the saure `functions' in every society at every period (e.g. Collis 1974a). The rOle of this particular form of physical wealth will rellect aspects of the society under consideration, and therefore the study of these artefacts should be, and is, going further than the purely numismatic (e.g. Aitchison 1988). It should also be noted that coin hoards are only one specific sort of hoard, and that the hoarding of other artefacts, occasionally in association with coins, continues (e.g. Reece 1988). 28.2 The numismatic background 28.2.1 Terminology An aspect of coinage studies with which computer users will be painfully familiar is the use of specialized terms. Below are brief definitions of those used in this paper. A hoard is simply two or more coins brought together in a deliberate manner (Casey 1986, p.51). A hoard is not, therefore, necessarily hidden. (Casey 1986, p.12). A coin is a form of money. Many objects have been used as money from gold rings to woolen blankets. Coins are currency when they are acceptable as a means of exchange, and this will be circumscribed by time and piace. An Athenian obol is a coin, and a form of money, but is no longer a form of currency. A modem thousand zlotys note is money, although not a coin, but is only currency in Poland. A cointype is one particular design of coin. Most coins are struck with a pair of dies. A cointype may be produced by one or more sets of dies. The obverse of a coin is usually the side which bears a portrait or symbol of the issuing authority, frequently accompanied by an inscription or legend. The reverse usually bears the type or design. The obverse die is set into an anvil when the coin is struck, while the reverse die is held in a pair of tongs and is struck with the hammer. An issue of coinage is a group of cointypes which can be seen to be related in some way. The coinage pool is all the coinage in circulation at any one time. Some coinages have a series of control marks. These usually consist of a series of letters, numbers or symbols and frequently can be related to the number of dies used in the production of a coin issue. 28.2.2 Coinage Studies The coinage of the Roman period has been a subject of detailed study for a considerable time. Although many areas are stili suhjects of contention for numismatists, the basic framework of date, piace of minting and type has been obtained, giving us a database of immense size and complexity. Numismatic study has already shown that within the Roman world coins performed different functions at different times. Inscriptions from Aezani show that by the fourth century the gold coinage had become worth its metallic value (Hendy 1984). The weight standard of the solidus basically remained steady from the Constantinian reform onwards. At the same time the silver and base metal coinage was constantly debasal and reformed. This two tiered coinage system is quite different from the structured Augustan system where it appears the State attempted to maintain a fixed ratio of value between coins regardless of the actual value of the metal contained within those coins. The rble of gold in the later Roman Empire is complicated (Kent 1956). Many state payments had to be made in gold, and therefore many taxes were payable only in gold (for example a Senatorial 'super-tax', Kent 1956, p. 195). It can be seen therefore that even within the limited realm of taxes and trade coinage can perform different functions. For ari overview and discussion of the many facets of `money' see Crump 1981 and Hart 1986. For many periods and areas, the literary and epigraphic evidence used (by Kent) in the above example does not exist. To assume that the situation is identical on an Empire-wide basis would be naive. It is obviously rarely possible to assess the function of coinage from the coins themselves. There- fore their associations, botti within the coin assemblage, and the archaeological record as a whole has to be consid-ered. The coinage evidence falls into two broad categories: site finds and hoards. Site finds represent casual losses. Intra-site spatial analysis may give some results. Inter-site comparisons and analysis have produced interesting results, although these can be difficult to interpret e.g. Hodder & Reece 1977, Reece 1982). These patterns, when seen on a regional scale, are essential in the interpretation of coin data from individuai sites (Casey 1974, 1980, 1986). Hoards, however, represent the deliberate collection, and usually deliberate deposition of coin. It is unlikely in most cases that the non-recovery of the hoard was as equally 195  KRIS LOCKYEAR deliberate and the possible reasons for non-recovery are quite varied. The analysis of coin hoards has been ap- proached in a variety of manners. For example, a frequent approach is to look at the distribution in time and space of hoards (e.g. Crawford 1969) although this approach has its limitations (Kent 1974, Casey 1986, pp. 61-63). Another approach is to look at the structure of coin hoards in more detail (e.g. Reece 1974). Recently, attempts have been made to take the interpretation of this evidence further (e.g. Creighton 1989). 28.3 Background to the simulation The context of the simulation was a study of 24 Roman Republican coin hoards (Lockyear 1989). These hoards were published in detail, although unfortunately not com- pletely, in Roman Republican Coinage (RRC. Crawford 1974). Much of the work concentrated on a number of issues not directly relevant to this paper, mainly revolving around Crawford's srcinal analyses which have been the centre of much criticism and debate (Hersch 1977, Mat-tingly 1977, Burnett 1987, Buttrey 1989). For this work the dating scheme of Crawford was taken l iterally, the later date being preferred if there was a date bracket. This allows cross-hoard comparisons although any use of the data which relied on actual calendar dates would have to consider the limitations of the evidence, and revisiona to Crawford's scheme. The structure of these coin hoards was examined and compared in detail by visual, and statistical methods in- cluding the use of correspondence analysis (Lockyear 1989, chapter 2). Figures 28.1 to 28.5 show some of the hoards plotted as histograms. As can be seen there is much variation in the data. The generai trends are similar for the first half or more of the histograms, but the pattern becomes much more varied in the latter half. 1 The hoard data were also plotted as a series of scattergrams with the percentage for each year, or for the hoards with a longer time span for each five years, plotted. A selection of these are given in Figs. 28.6 to 28.8. From these, and the other analyses, it became apparent that these hoards conformed to the pattern noticed by Reece when looking at the hoard evidence for Britain (Reece 1974, cf. Lockyear 1989, pp. 16-23). In generai, hoards with broadly the same closing date have a very similar pattern in the representation of the earlier coins, but this pattern varies grcatly towards the closing date. There are a number of pos- sible reasons for this variation all of which have interesting implications for the way coin was circulating, being saved, and therefore, being used. Hoards with few coins minted near to its closing date have been categorised in the past as savings hoards and those with large numbers of those coins (e.g. Fiesole, see Figs. 28.1 and 28.6) as emergency hoards. These categories implicitly explain this variation in terms of the period of time over which the hoard was collected. Emergency hoards are collected and deposited rapidly. For example, a days takings at a market stall. Savings hoards on the other hand are collected over a longer period of time and therefore have lower numbers of the most recent coins. In order to avoid this implicit explanation I have called these two categories Type One, and Type Two. Recently, other interpretations of this pattern have been put forward (Creighton 1989). However, no attempt to my knowledge had been made to assess the effects of the various factors which may produce this pattern. If we are to ever to use the coin evidence to reveal aspects of the society that uses the coinage, we must have some idea as to how the patterns we observe may have been produced. A simulation program was used to try and fili the gap. 28.4 The simulation program The program firstly has to simulate the coinage in circulation at any one time and piace, and then has to simulate the processes of collection of the hoard. In order to do this the program needs a number of pieces of information: 1. The number of obverse dies used per annum. 2. The number of coins minted per obverse die. 3. The introduction delay.' 4. The decay rate. 5. The type of hoard. 6. The size of the hoard. 7. For a Type One hoard, its date, for a Type Two hoard its start and end dates. Items 1 and 2 have been matters for intense numismatic debate. The number of obverse dies used per annum in the simulation is derived from a modi fication of Crawford's srcinai method (Lockyear 1989, section 2.3; cf. Crawford 1974; see page 200). The number of coins minted per obverse die was kept constant at 30,000. 2 This is the figure used by Crawford, and is again a matter for debate. Minting experiments suggest a lower figures of 10,000 coins per dìe (Sellwood 1963). This figure will not affect the results un- less this number is set at an unrealistically low figure, or the number of coins collected is set at an equally unrealistically high figure. Item 3 encapsulates a number of factors. These are: 1) the delay in the release of coin from the mint, 2) the speed of circulation and 3) the distance from the area where the coinage is introduced into the pool from the area where the coin hoard is being collected (see Fig. 28.9). Item 4 is simply the number of coins lost per year. The figure of 2% calculated by Patterson for American silver coinage has been used in a number of other studies (Pat- terson 1972, Hopkins 1980). Preston (1983) calculated this value using a regression technique in order to be able to apply an `age correction' to hoards when comparing them. This paper has, however, some serious flaws and will be discussed in detail in a future article. The type of the hoard reflects the manner in which it was collected, see page 196. The two types are extreme theoretical examples and real hoarding practices are likely to be much more complex. The coins are collected randomly from the coinage pool for the appropriate year(s) calculated on the basis of the other parameters discussed above. The selection of coins for hoarding in reality is not a completely 'Some of the recurrent gaps in the data are a result of Crawford's dating scheme. cf. Mattingly 1977, p. 203. 2 The reason for using the obverse die totals is that the obverse die which was set in the anvil lasted longer than the reverse. 196  SIMULATING COIN HOARD FORMATION 8. 700 - 400 - No o   de.   /n 200 - 100 - 50 - 10 - 0 9 8 7 6 5 4 year (B.C.) Figure 28.1: Fiesole n = 1976, mar(y) = 700 700 - 400 - No o   de.   V 200 - 100 - 50 - 10 - o 150 140 130 120 110 100 9 8 7 6 5 4 year (B.C.) Figure 28.2: Monte Codruzzo n = 4471, mar(y) = 633 random process. In this example, the coins being hoarded are all silver den rii and not the bronze denominations also being minted at the time. We also have the advantage that the coins were of a stable weight and fineness during this period. In other periods the selection of coins for hoarding is greatly influenced by the individuai coins metallic content and weight. However, if all other factors are equa), the choice of coins for hoarding can be seen to be random selections from the coinage pool (e.g. Thordeman 1948). The program as it stands now was written with a very specific task in mind and therefore will only dea) with the period 156 to 50 BC. Output is limited to a listing, and the data summarized as PICTEX scattergrams for inclusion in LATEX documents. The program is initialised with the simple command simulate. The user is prompted for a number of pieces of information (see Fig. 28.10). Having read in the die data, and the Introduction delay' factor the program constructs a series of battleship curves for each year's coinage which are fed finto a two dimensiona) array. The coinage pool for each year from which coins will be collected is then calculated. The actual hoarding process is then simulated by simple random selection of coins from the pool. In the case of a Type One hoard the total number of coins requested will be collected as a series of random choices from the pool calculated for that year. For a Type Two hoard the opening and closing dates for the hoard are inputted, as well as the total number of coins that will be finally in the hoard. The program then collects the appro- priate number of coins randomly from the coinage pool for each year. 28.5 The results As with all simulations the number of possible variations that could be tested is immense. It was decided therefore 197  7 6 5 0 40 00 90 year (B.C.) Figure 28.3: Casaleone n = 709, max(y) = 49 120 10 200 - 400 - No o   n   100 - 50 - 10 0 40 30 120 110 100 90 year (B.C.) Figure 28.4: San Giuliano Vecchio n = 1718, max(y) = 150 80 KRIS LOCKYEAR 50 - 10 - èrI k 150 No o   n   0 200 100 o 50 - z 10 - O 130 120 110 100 90 80 70 60 50 40 year (B.C.) Figure 28.5: Alvignano n = 2334, max y) = 307 to concentrate on the effects of three factors, the coinage `decay rate,' the `introduction delay,' and the manner of hoarding. Each of there was varied whilst keeping the other factors constant. Results from a number of runs using the same parameters showed that there was remarkably little variation between each run. 3 In order to enable a certain amount of comparison the size and date of two of the hoards studied were used. At this point I must emphasise a number of points. 1. As the die figures used in this program are derive(' from the hoards, any similarity between the simulated hoards and the real ones may contain a degree of circularity. 2. The simulation will not explain the factors which pro- duced the hoard structure observed. The simulation by necessity is a simplification of the rea' situation, and it would probably be possible to replicate the observed hoard structure in a number of ways. 3. As a result of the above, it is invalid to attempt any statistical comparison or correlation between the real, and the simulated hoards. It is not, therefore, worth writing the program in such a way that it alters its own parameters until it finds the closest o a real hoard. 4. This program must be seen as a first step in the study of coin hoard formation, and not a definitive statement. Firstly, the effects of altering the period of time taken to collect the hoard was examined. So that the simulated hoards could be compared with real hoards, the date and size of two of the hoards were used as parameters. These were the Fiesole hoard, as an extreme example of a hoard with high closing figures, and the San Giuliano Vecchio hoard as 3 The random number generation was checked very carefully in case it was inadeguate. The similarity is easily explained when one notes the limited number of possible choices (107) and the large number of selections (minimum in this study of 1716). llis effect is increased when the dominante of some issues is noted, and the faci that the coins are ploued in five year groups. Smaller hoards, or those plotted by individuai year, show less similarity. 198
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